Imaging spectrometer based on acousto-optic tunable filter (AOTF) is widely used in hyperspectral remote sensing at present, however, recently experiment results has shown that changes of environment temperature would cause driving frequencies corresponding different spectral position, result in rising of spectral curve measurement error . In order to make clear changes of AOTF spectral tuning curve under the different environment temperatures, it is necessary to develop an environmental temperature controlled calibration device, and perform plenty spectral calibration under different environment. According to the experiment results, temperature correction is considered in the spectral calibration result. The results show that the spectral characteristics of AOTF imaging spectrometer would be changed by the difference of environmental temperatures. Therefore uncertainty of spectral calibration would be decreased by temperature parameter added into the spectrum of calibration results. Therefore, the outfield measurement error would be decreased and precision of imaging spectrometer spectral measurement would be improved according to this discovery.

An incoherent broadband cavity enhanced UV-LED spectrometer (IBBCEAS) was developed to detect atmospheric HONO and NO₂. Using a UV light emitting diode (LED) operating at ~ 366 nm in combination with a high finesse optical cavity, HONO and NO₂ were able to be simultaneously measured with high sensitivity. Detection limits (for SNR=1) of 0.3 ppbv for HONO and 1 ppbv for NO₂ were achieved with an optimum acquisition time of 120 s. Stability of the developed cavity enhanced UV-LED spectrometer has been characterized by means of an Allan variance analysis. Daytime and nighttime concentrations of atmospheric HONO and NO₂ were measured and compared with data from LOPAP for HONO and blue light converter-based NOx analyzer for NO₂. The present work performed in a real atmospheric environment demonstrates the feasibility of using IBBCEAS technique for interference (chemical and spectral) free measurement of HONO. Experimental detail will be presented, the problems encountered during the real atmospheric measurement will be discussed.

The degree of linear polarization (DOLP) expressions at any polarizer direction (PD) was deduced based on the Stokes vector and Mueller matrix. The outdoors experiments were carried out to demonstrate the expressions. This paper mainly explored the DOLP-image-Contrast (DOLPC) between the target image and the background image, and the PD and RGB waveband that be considered two important influence factors were studied for camouflage target polarization detection. It was found that the DOLPC of target and background was obviously higher than intensity image. When setting the reference direction that polarizer was perpendicular to the incident face, the DOLP image of interval angle 60 degree between PD and reference direction had relatively high DOLPC, the interval angle 45 degree was the second, and the interval angle 35 degree was the third. The outdoors polarization detection experiment of controlling waveband showed that the DOLPC results was significantly different to use 650nm, 550nm and 450nm waveband, and the polarization detection performance by using 650nm band was an optimization method.

Temperature and strain measurement accuracy of Brillouin distributed optical fiber sensor are easily influenced by the performance of laser light source. A wavelength/power DFB semiconductor laser light source with high-stability used for Brillouin Fiber Sensing is designed. The laser light source works with constant-current drive circuit and temperature control circuit, which precisely controls drive current and operating temperature of the DFB semiconductor laser and makes the wavelength and optical power under control. The results show that: (1) the optical wavelength increases about 0.1nm and the power reduces about 0.05dBm when the temperature increased by 1°C . (2) The maximum drift of wavelength is 0.012nm and the maximum drift of optical power is 0.05dBm (0.0014mW) at 25 °C within one hour. The laser light source can completely meet the demand for Brillouin distributed optical fiber sensing.

Aiming at the noise vulnerability and the low detection performance of the classical RX algorithm under the complex background, an improved RX-OSP hyperspectral anomaly detection method is proposed. Firstly, PCA dimension reduction method is applied to suppress the background of hyper-spectral image. Secondly, RX operator is used to detect the pixels owning the most prominent anomaly and the pixels are projected to their orthogonal complement subspaces. Then RXOSP processing is repeated according to the foregoing steps until there is no obvious anomaly. During the process of detection, the covariance matrix is calculated by localization instead of the traditional global approach to reduce the false detection effectively. Finally, ROC curve is adopted as the evaluation index for the experiment results, which reveals that the improved RXOSP algorithm is superior to RX, PCA-RX and RXOSP algorithms.

With the advantages of fluorescence excitation and environmental adaptability simultaneously, Ultraviolet Image Spectroscopy has shown irreplaceable features in the field of latent target detection and become a current research focus. A design of Large Aperture Ultraviolet Spatially Modulated Imaging Spectrometer (LAUV-SMIS) based on image plane interferometer and offner system was first proposed in this paper. The data processing technology of time-spatial modulation FTIS in UV band has been studied. The latent fingerprint could be recognized clearly from the image since which is capable to meet the need of latent target detection. The spectral curve of the target could distinguish the emission peak at 253.7nm and 365nm when the low pressure and high pressure mercury lamp were used as the illuminator. Accurate spectral data of the target can be collected on the short and long wave ends of the working band.

A sonobuoy system based on a fiber optic vector hydrophone (FOVH) is demonstrated. Phase Generated Carrier– Arctangent (PGC-ATAN) demodulation algorithm was used to acquire real-time underwater acoustic signals. After the optimal design of the laser configuration, the background noise of the FOVH is -104.3dB re rad √ Hz at 1 kHz, with an acceleration sensitivity of 41.5dB re rad/g which allows the system detecting signals at DSS0. The theoretical derivation of FOVH directivity is proposed and the design criterion is discussed. The ratio of the minimum to the maximum amplitude of the FOVH directivity is -35dB by symmetrical structure design of the FOVH. A lake trial shows that the maximum detection range of the sonobuoy system is more than 15km for an acoustic signal of 210dB re μPa, and the bearing of a moving target can be estimated.

Tree species distribution is an important issue for sustainable forest resource management. However, the accuracy of tree species discrimination using remote-sensing data needs to be improved to support operational forestry-monitoring tasks. This study aimed to classify tree species in the Liangshui Nature Reserve of Heilongjiang Province, China using spectral and structural remote sensing information in an auto-mated Random Forest modelling approach. This study evaluates and compares the performance of two machine learning classifiers, random forests (RF), support vector machine (SVM) to classify the Chinese high-resolution remote sensing satellite GF-1 images. Texture factor was extracted from GF-1 image with grey-level co-occurrence matrix method. Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Enhanced Vegetation Index (EVI), Difference Vegetation Index (DVI) were calculated and coupled into the model. The result show that the Random Forest model yielded the highest classification accuracy and prediction success for the tree species with an overall classification accuracy of 81.07% and Kappa coefficient value of 0.77. The proposed random forests method was able to achieve highly satisfactory tree species discrimination results. And aerial LiDAR data should be further explored in future research activities.

In view of the existing increasingly perfect jamming methods, we bring up a novel method of detection based on the aircraft charged characteristics, which uses the electric charges carried by flying target inevitably during its flight. The charges cannot be detected target by the photoelectric method to interference and coverage characteristics. By analyzing the charge characteristic and the relative relationship between detector and the target, we put forward a target detection equation based on this method, validates it with simulation experiment, and obtains the novel method of detection then.

In order to improve land cover classification accuracy of the coastal tidal wetland area in Dafeng, this paper take advantage of hyper-spectral remote sensing image with high spatial resolution airborne Lidar data. The introduction of feature extraction, band selection and nDSM models to reduce the dimension of the original image. After segmentation process that combining FNEA segmentation with spectral differences segmentation method, the paper finalize the study area through the establishment of the rule set classification of land cover classification. The results show that the proposed classification for land cover classification accuracy has improved significantly, including housing, shadow, water, vegetation classification of high precision. That is to say that the method can meet the needs of land cover classification of the coastal tidal wetland area in Dafeng. This innovation is the introduction of principal component analysis, and the use of characteristic index, shape and characteristics of various types of data extraction nDSM feature to improve the accuracy and speed of land cover classification.

The unmanned airborne (UAV) laser spectrum radar has played a leading role in remote sensing because the transmitter and the receiver are together at laser spectrum radar. The advantages of the integrated transceiver laser spectrum radar is that it can be used in the oil and gas pipeline leak detection patrol line which needs the non-contact reflective detection. The UAV laser spectrum radar can patrol the line and specially detect the swept the area are now in no man's land because most of the oil and gas pipelines are in no man's land. It can save labor costs compared to the manned aircraft and ensure the safety of the pilots. The UAV laser spectrum radar can be also applied in the post disaster relief which detects the gas composition before the firefighters entering the scene of the rescue.

In this paper, we propose a novel algorithm for accurately estimating the degree of radiometric non-uniformity in remote sensing images. The algorithm was tested on high-quality images and heavily striping images, and quantitative analyses were conducted to evaluate the performance for each band by measuring the radiometric non-uniformity of the images. The results demonstrated that the proposed algorithm exhibits high accuracy and stability compared with traditional algorithms. The radiometric performance of TianGong-1 short-wave infrared images was calculated using this new method, and it was highly correlated with the solar angle, pitch angle and refrigerator thermal according to the Apriori algorithm. Based on these results, we have proposed a strategy for restricting increases in striping.

With the increasing number of vehicles, the harm from NO to the environment becomes more and more prominent. So the monitoring of the NO concentration of the vehicle exhaust emissions is very important to assess the emission levels. In this paper, the NO detection system designing for vehicle exhaust emissions based on the non-dispersive ultraviolet principle (NDUV) has been researched. The technical indexes of the two-way modulation UV signal detection circuit are discussed in detail. And then a precision detection circuit is designed, which is composed of a trans-impedance amplifier and a lock-in amplifier, with which the output of the UV photoelectric detector can be amplified to a suitable voltage range, and the DC noise of the pre-stage amplifier is effectively removed by the lock-in amplifier. An experimental system was set up to test the designed circuit. To ensure the consistency of the two channels, the method of exchange calibration was adopted in the test. It’s drawn that the designed circuit is of high SNR, measuring accuracy and a large dynamic range from the test results. The NO concentration detection limit of vehicle emissions can reach 1ppm, and the detection precision is ±15ppm.

3-5 μm mid-infrared wave band is in the atmosphere window, it has lots of promising applications on the spectroscopy, remote sensing, medical treatment, environmental protection and military affairs. So, it has been a hot topic around the world to research the lasers at this wave band. In recent years, adiabatic passage technology has been applied in frequency conversion area, which borrowed from atomic physics. In this paper we will introduce efficient nonlinear optics frequency conversion by suing this technology.

Fourier transform infrared spectrometry can be used for harmful gases online detection, Due to the overlap and mutual interference between the absorption spectra, accurate detection is very difficult for the mixture gas. In the wave number of 400~2200 cm^-1, the infrared absorption spectrum characteristics of typical harmful gases in underground closed environment were analyzed, the position of characteristic absorption peak was studied, and the infrared absorption frequency of each harmful gas was determined. The reasonable detection scheme was designed, and the mutual interference of the detection was eliminated. The harmful gases include SO₂, CO₂, H₂S, NH₃, NO₂, CO, O₃, H₂CO, C₆H₆ and H₂O.

Daliuta mining coal fires at Inner Mongolia were not reported at present in remote sensing. However, they still pose a serious threat to the surroundings. In order to extract combustion range of the coal mine, we used the wintertime thermal airborne infrared hyperspectral images of TASI acquired in 2016 to detect the coal fire of Daliuta mining. The synchronous in situ measured temperature was used to establish space-to-ground regression equation with the image temperature for retrieving land surface temperature. Extracted coal fire through the reasonable threshold by the processed image data, identified a region where the surface temperatures was -0.5°C to 300°C. MODTRAN4 code was used to estimate the upward and downward radiation and transmission of the atmosphere. On this basis, the non-coal fire anomaly areas, such as the cooling water of power plant, heat buildings, chimney, were separated from the coal fire heat anomaly areas by the characteristic difference of the emissivity spectrum in the objectives. The results show that the bands 1-16 of TASI are suitable for infrared inversion temperature for the coalfield fire. There was a linear relationship between synchronous in situ observation temperature and the image temperature, and the determination coefficient R² was 0.9938. The extracted coal fire anomaly range is able to provide some decision support for underground coal fire extinguishing. A detailed fire map of shallow coal areas can help to prioritize fire fighting operations in order to avoid the chance of starting a new coal fire.

Hyperspectral Light Detection And Ranging (Hyperspectral LiDAR), a recently developed technique, combines the advantages of the LiDAR and hyperspectral imaging and has been attractive for many applications. Supercontinuum laser (SC laser), a rapidly developing technique offers hyperspectral LiDAR a suitable broadband laser source and makes hyperspectral Lidar become an installation from a theory. In this paper, the recent research and progressing of the hyperspectral LiDAR are reviewed. The hyperspectral LiDAR has been researched in theory, prototype system, instrument, and application experiment. However, the pulse energy of the SC laser is low so that the range of the hyperspectral LiDAR is limited. Moreover, considering the characteristics of sensors and A/D converter, in order to obtain the full waveform of the echo, the repetition rate and the pulse width of the SC laser needs to be limited. Recently, improving the detection ability of hyperspectral LiDAR, especially improving the detection range, is a main research area. A higher energy pulse SC laser, a more sensitive sensor, or some algorithms are applied in hyperspectral LiDAR to improve the detection distance from 12 m to 1.5 km. At present, a lot of research has been focused on this novel technology which would be applied in more applications.

While hyperspectral data shares rich spectrum information, it has numbers of bands with high correlation coefficients, causing great data redundancy. A reasonable band selection is important for subsequent processing. Bands with large amount of information and low correlation should be selected. On this basis, according to the needs of target detection applications, the spectral characteristics of the objects of interest are taken into consideration in this paper, and a new method based on spectrum difference is proposed. Firstly, according to the spectrum differences of targets of interest, a difference matrix which represents the different spectral reflectance of different targets in different bands is structured. By setting a threshold, the bands satisfying the conditions would be left, constituting a subset of bands. Then, the correlation coefficients between bands are calculated and correlation matrix is given. According to the size of the correlation coefficient, the bands can be set into several groups. At last, the conception of normalized variance is used on behalf of the information content of each band. The bands are sorted by the value of its normalized variance. Set needing number of bands, and the optimum band combination solution can be get by these three steps. This method retains the greatest degree of difference between the target of interest and is easy to achieve by computer automatically. Besides, false color image synthesis experiment is carried out using the bands selected by this method as well as other 3 methods to show the performance of method in this paper.

In this paper, the decision tree classification based on the CART algorithm (Classification and Regression Tree) is used to extract the impervious surface area of Nantong city in Jiangsu Province in China. Impervious surface dynamic change nearly 25 years in Nantong city is researched using four periods Landsat images of 1990, 2003, 2008, and 2014. The results show that the classification precision based on the CART algorithm is higher, which can more accurately extract the impervious surface. During the 25 years, the trend of the impervious surface of Nantong is increased year by year. Urban construction and expansion is one of the driving forces of the impervious surface increase.

Based on the range-gated technology in the application of airborne laser detection imaging system, sequence relations of the parameters in the model of range gating in detail was analyzed. The effective scope of atmospheric backscatter and the moment before or after pulsing of the unit section was obtained. Horizontal range-gated imaging model was established and the calculation method of backscattered light intensity was given. Then slant distance detection of airborne laser active imaging was revised. By the object-image relation of points on the scattering section, the light path diagram of imaging detection system was built and the corresponding relation of light intensity between scattering points and receiving points was given. The variation regularity and distribution of light intensity on the detector under horizontal and slant detection were gained. Under the idea, the outfield testing platform was set up, and the relative error of data between measured and simulated results was controlled within 5%. The two kinds of data achieved a good coincident, which demonstrated the effectiveness of the built model. The two kinds of data achieved a good coincident, which demonstrated the effectiveness of the built model.

During the research of hyper-spectral imaging spectrometer, how to process the huge amount of image data is a difficult problem for all researchers. The amount of image data is about the order of magnitude of several hundred megabytes per second. Traditional solution of the embedded hyper-spectral data processing platform such as DSP and FPGA has its own drawback. With the development of GPU, parallel computing on GPU is increasingly applied in large-scale data processing. In this paper, we propose a new embedded solution of hyper-spectral data processing platform which is based on the embedded GPU computer. We also give a detailed discussion of how to acquire and process hyper-spectral data in embedded GPU computer. We use C++ AMP technology to control GPU and schedule the parallel computing. Experimental results show that the speed of hyper-spectral data processing on embedded GPU computer is apparently faster than ordinary computer. Our research has significant meaning for the engineering application of hyper-spectral imaging spectrometer.

A small target is a target which is far enough to a detector, and its image on FPA can’t be large enough to show its shape and size. In this situation, when a small target is detected by an infrared imaging single-band detector, we can only analyze it by the dispersion point or the subpixel image caused by it. The target discrimination can be impossible when it meets a smaller-sized target with higher temperature and a larger-sized target with lower temperature, because their image on FPA can be quite similar when they’re far enough. However, with the dual-band detection, we can figure out the temperature via dual-band ratio easily, without the information of distance and target size. Equivalent area can be also figure out during this calculation. The target discrimination can be achieved with the temperature and equivalent area known. And according to some priori knowledge, can we make those target recognized in a particular scene. This article briefly show the benefit of dual-band detection compared to single band detection.

Traditional method of water quality sampling is time-consuming and highly cost. It can not meet the needs of social development. Hyperspectral remote sensing technology has well time resolution, spatial coverage and more general segment information on spectrum. It has a good potential in water quality supervision. Via the method of semi-analytical, remote sensing information can be related with the water quality. The inherent optical properties are used to quantify the water quality, and an optical model inside the water is established to analysis the features of water. By stochastic optimization algorithm Threshold Acceptance, a global optimization of the unknown model parameters can be determined to obtain the distribution of chlorophyll, organic solution and suspended particles in water. Via the improvement of the optimization algorithm in the search step, the processing time will be obviously reduced, and it will create more opportunity for the increasing the number of parameter. For the innovation definition of the optimization steps and standard, the whole inversion process become more targeted, thus improving the accuracy of inversion. According to the application result for simulated data given by IOCCG and field date provided by NASA, the approach model get continuous improvement and enhancement. Finally, a low-cost, effective retrieval model of water quality from hyper-spectral remote sensing can be achieved.

As the resolution for multispectral images continuously improving, the phenomena that "synonyms spectrum", "foreign body with spectrum" become more apparently, therefore, in the results for pixel-based iteratively reweighted multivariate alteration detection (IR -MAD) may appear many issues such as broken patch, much pseudo-change, more noise, the low overall detection rate et al. In order to improving the above problems. In this paper, the pixel-based IR-MAD algorithm can be transferred to the object domain. the object-based IR-MAD(OB_IRMAD) method apply different and meaningful combinations of features rather than the original pixels. these features are classified into meaningful combinations, so that the results for object-based change detection can get higher reliability and accuracy. To stabilize solutions to the IR-MAD problem, some of regularization may be needed. A case with ZY-3 multispectral image at one point in time in province of Xinjiang border port demonstrate the effectiveness and feasibility of the OB_IRMAD. Compared as, using the same date and region we do the pixel-level IR-MAD change detection and artificial visual change detection. Finally, we calculate the various evaluation indexes for accuracy utilizing confuse matrix and compare the accuracy of the two detection results. The results show: in the ways of overall accuracy, correct rate, error rate, the OB_IRMAD is better than pixel-level IRMAD, change polygon more rules and indicates a less noisy.

Imaging spectrometer plays an important role in the remote sensing application. Imaging spectrometer can collects and provides a unique spectral signature of many materials. The spectral signature may be absorbing, reflecting, and emitting. Generally, optical spectral bands for earth observing consist of VNIR, SWIR, TIR/LWIR. VNIR band imaging spectrometer is well-known in vegetation remote sensing and ocean detection. SWIR band imaging spectrometer is widely applied in mineralogy investigation. For its uniquely capability of spectral radiance measurement, TIR/LWIR imaging spectrometer attracts much attention these years. This paper will present a new generation VNIR/SWIR/TIR imaging spectrometer. The preliminary result of its first flight will also be shared. The spectral sampling intervals of VNIR/SWIR/TIR are 2.4nm/3nm/30nm, respectively. The spatial pixel numbers are 2800/1400/700,respectively. It’s a push-broom imaging spectrometer.

The main ingredient of mash gas is alkenes, and methane is the most parts of mash gas and ethane is a small portion of it. Fast, accurate, real-time measurement of methane and ethane concentration is an important task for preventing coal mining disaster. In this research, a monitoring system with tunable diode laser absorption spectroscopy (TDLAS) technology has been set up for simultaneous measurement of methane and ethane, and a DFB laser at wavelength of 1.653μm was used as the laser source. The absorption spectroscopy information of methane and ethane, especially the characteristic of the spectrum peak positions and relative intensity were determined by available spectral structures from previous study and available database. Then, the concentration inversion algorithm method based on the spectral resolution and feature extraction was designed for methane and ethane synchronous detection. At last, the continuously experimental results obtained by different concentration of methane and ethane sample gases with the multiple reflection cell and the standard distribution system. In this experiment, the standard distribution system made with the standard gas and two high precision mass flow meters of D07 Sevenstar series whose flow velocity is 1l/min and 5l/min respectively. When the multiple reflection cell work stably, the biggest detection error of methane concentration inversion was 3.7%, and the biggest detection error of ethane was 4.8%. So it is verified that this concentration inversion algorithm works stably and reliably. Thus, this technology could realize the real-time, fast and continuous measurement requirement of mash gas and it will provide the effective technical support to coal mining production in safety for our country.

In this paper, the reconstruction of axisymmetric temperature and H₂O concentration distributions in a flat flame burner is realized by tunable diode laser absorption spectroscopy (TDLAS) and filtered back-projection (FBP) algorithm. Two H₂O absorption transitions (7154.354/7154.353 cm^-1 and 7467.769 cm^-1) are selected as line pair for temperature measurement, and time division multiplexing technology is adopted to scan this two H₂O absorption transitions simultaneously at 1 kHz repetition rate. In the experiment, FBP algorithm can be used for reconstructing axisymmetric distributions of flow field parameters with only single view parallel-beam TDLAS measurements, and the same data sets from the given parallel beam are used for other virtual projection angles and beams scattered between 0° and 180°. The real-time online measurements of projection data, i.e., integrated absorbance both for pre-selected transitions on CH₄/air flat flame burner are realized by Voigt on-line fitting, and the fitting residuals are less than 0.2%. By analyzing the projection data from different views based on FBP algorithm, the distributions of temperature and concentration along radial direction can be known instantly. The results demonstrate that the system and the proposed innovative FBP algorithm are capable for accurate reconstruction of axisymmetric temperature and H₂O concentration distribution in combustion systems and facilities.

A new method to detect ship target at sea based on improved segmentation algorithm is proposed in this paper, in which the improved segmentation algorithm is applied to precisely segment land and sea. Firstly, mean value is replaced instead of average variance value in Otsu method in order to improve the adaptability. Secondly, Mean Shift algorithm is performed to separate the original high spatial resolution remote sensing image into several homogeneous regions. At last, the final sea-land segmentation result can be located combined with the regions in preliminary sea-land segmentation result. The proposed segmentation algorithm performs well on the segment between water and land with affluent texture features and background noise, and produces a result that can be well used in shape and context analyses. Ships are detected with settled shape characteristics, including width, length and its compactness. Mean Shift algorithm can smooth the background noise, utilize the wave’s texture features and helps highlight offshore ships. Mean shift algorithm is combined with improved Otsu threshold method in order to maximizes their advantages. Experimental results show that the improved sea-land segmentation algorithm on high spatial resolution remote sensing image with complex texture and background noise performs well in sea-land segmentation, not only enhances the accuracy of land and sea boarder, but also preserves detail characteristic of ships. Compared with traditional methods, this method can achieve accuracy over 90 percent. Experiments on Worldview images show the superior, robustness and precision of the proposed method.

A miniaturized hyper-spectral imager is enabled with image sensor integrated with dispersing elements in a very compact form factor, removing the need for expensive, moving, bulky and complex optics that have been used in conventional hyper-spectral imagers for decades. The result is a handheld spectral imager that can be installed on miniature UAV drones or conveyor belts in production lines. Eventually, small handhelds can be adapted for use in outpatient medical clinics for point-of-care diagnostics and other in-field applications.

Hyperspectral imaging is a new technology for nondestructive detection of fruit which developed rapidly in recent years. It can get the image and spectral information of the detected object from "three-dimensional", can also reflect the internal and external qualities simultaneously, is a new efficient grading method for fruit. This article induces system types by introducing the hardware structure, determine light source and scanning mode which apply to apple grading. We describe detecting process of apple external and internal indicators according to two directions in apple grading. For internal quality detection, we generalize the methods of image enhancement and image segmentation. For quality indicator detection, we elaborate the process of system calibration and spectral preprocessing, also we discuss the significance of optimal band selection, classify the methods of prediction model establishment and evaluation. Then we summarize the domestic and foreign research results of several main indicators of apple grading, the external quality including color, size, slight injury and contamination, and the internal quality including soluble solids content (SSC), firmness and bruise. We illustrate accuracy, modeling methods, research progress of all indicators and express personal comments for the research progress of single indicator. Finally, this article proposes the deficiency, research direction and application prospect of hyperspectral imaging in apple nondestructive detection and grading.

With unique working principle and spectral characteristic, the long wave infrared (LWIR) interferometric spectral imaging is a popular technology with wide application in many fields. In order to miniaturize and light the instrument, a new method of LWIR spectral imaging system based on a variable gap Fabry-Perot (FP) interferometer is researched. With the system working principle analyzed, theoretically, it is researched that how to make certain the primary parameter, such as, the reflectivity of the two interferometric cavity surfaces and the wedge angle of interferometric cavity. A prototype is developed and good experimental results of blackbody and polypropylene film are obtained. The research shows that besides high throughput and high spectral resolution, the advantage of miniaturization is also simultaneously achieved in this method.

According to the information optics theory, a novel watermarking method based on Fourier-transformed digital holography and singular value decomposition (SVD) is proposed in this paper. First of all, a watermark image is converted to a digital hologram using the Fourier transform. After that, the original image is divided into many non-overlapping blocks. All the blocks and the hologram are decomposed using SVD. The singular value components of the hologram are then embedded into the singular value components of each block using an addition principle. Finally, SVD inverse transformation is carried out on the blocks and hologram to generate the watermarked image. The watermark information embedded in each block is extracted at first when the watermark is extracted. After that, an averaging operation is carried out on the extracted information to generate the final watermark information. Finally, the algorithm is simulated. Furthermore, to test the encrypted image’s resistance performance against attacks, various attack tests are carried out. The results show that the proposed algorithm has very good robustness against noise interference, image cut, compression, brightness stretching, etc. In particular, when the image is rotated by a large angle, the watermark information can still be extracted correctly.

Hyperspectral remote sensing technology has been in front of remote sensing science and technology. It brought a technical revolution for remote sensing. Hyperspectral remote sensing let the spatial and spectral dimensions of traditional image information fusion to an organic whole. It make the multispectral remote sensing image features in wide band to be detected and differentiated in hyperspectral remote sensing detection. Hyperspectral mineral mapping is the most successful technology which can exert its advantages of application field in geology. Using the airborne visible-light and near infrared and short-wave infrared imaging spectral HyMap data, we research the rock ore information recognition of Hami district in Xinjiang. Hyperspectral mineral mapping has made the good application effect in the exploration and resource prediction evaluation in ore-prospecting work.

USB 3.0 specification was published in 2008. With the development of technology, USB 3.0 is becoming popular. LVDS(Low Voltage Differential Signaling) to USB 3.0 Adapter connects the communication port of spectrometer device and the USB 3.0 port of a computer, and converts the output of an LVDS spectrometer device data to USB. In order to adapt to the changing and developing of technology, LVDS to USB3.0 Adapter was designed and developed based on LVDS to USB2.0 Adapter. The CYUSB3014, a new generation of USB bus interface chip produced by Cypress and conforming to USB3.0 communication protocol, utilizes GPIF-II (GPIF, general programmable interface) to connect the FPGA and increases effective communication speed to 2Gbps. Therefore, the adapter, based on USB3.0 technology, is able to connect more spectrometers to single computer and provides technical basis for the development of the higher speed industrial camera. This article describes the design and development process of the LVDS to USB3.0 adapter.

We investigate the surface plasmon resonance in a new composite nanostructure (Nanowires array beneath metal film). Computational simulation results exhibit that, for both transverse electric(TE) and transverse magnetic (TM) polarization, the positions of resonance peaks is extremely sensitive to the change of center distance (Filling ratio of nanowires). When the diameter of Nanowires is 4nm and under TM polarization, the resonance angle increasing with the increase of center distance. In the case of TE polarization, the result is completely the opposite within limits. It is also shown that changes in thickness of Ag film(At the top of the Ag nanowire) has little direct effect on the resonance angle, But the characteritics of SPR intensity is influenced by the thickness of Ag film in the most degree. When the thickness of Ag film is 50 nm, In range of 10nm to 100nm, the minimum value of the reflectance is only 0.05, the result is consistent with the previous studies. Additionally, the nano composite structure material is very sensitive to the refractive index change of the lowest layer when under the TE- polarization. we have done mode analysis of the SPR structure for both simple and practical structures using comsol multiphysics, our approach is intend to show the feasibity and extend the applicability of the plasmonic nanowires, could lead to provide the basis for design the new structure of nanowires array.

CASI/SASI data covering the hongliugou hydrothermal metallic deposit, western altyn tagh mining districts, were used to map hydrothermal alteration minerals. The hyperspectral data were calibrated to apparent surface reflectance using the empirical line method and the method of radiative transfer model. The three methods of estimating apparent surface reflectance have been evaluated by using field spectral. Two spectral unmixing algorithms (MTMF and CEM) were applied to analyse imaging spectrometer data. The objective was to compare the performance of the two algorithms, in order to map surface hydrothermal alteration mineralogy in the hongliugou area. Altered rocks and faults in the district can be discriminated by use of mineral distribute images derived from CASI/SASI data.

In this work, the workflow of airborne thermal infrared hyperspectral technology in the actual application process is reviewed. Using the Thermal Airborne Spectrographic Imager (TASI-600), a hyperspectral thermal infrared imager manufactured by ITRES Research Limited as a case study, the work process including instrument calibration, collecting the region information of interest, data processing and analysis is elaborated. The value and effect using thermal infrared data obtained through TASI-600 is demonstrated. This work provides ideas and references for further study and investigation on the application of airborne thermal infrared hyperspectral remote sensing.

Haze always exists in hyper-spectral remote sensing imagery, and it is a key reason that influences the effective information extraction of hyper-spectral images. Specially, when the faint haze covers part of the target in remote sensing images, the target still can be detected but not clear. So, how to remove the influence of the haze and improve the applicable efficiency of hyper-spectral images is a popular research point. This paper proposes a dehazing method for hyper-spectral images based on linear unmixing. First, a popular hyper-spectral unmixing method called FUN is used to get the signature of all the end-members and their corresponding abundance. And then, the abundance of the haze end-member is removed and the abundances of the rest end-members are adjusted to satisfy the sum-to-one and non-negative constraint. Lastly, the new abundance and the signature of the end-members are linearly mixed to get the dehazed hyper-spectral images. The experiment result shows that the dehazed hyper-spectral images exhibit better target information and details. The method is effective and available.

Quantitative analysis of the simulated complex oil spills was researched based on PSO-LS-SVR method. Forty simulated mixture oil spills samples were made with different concentration proportions of gasoline, diesel and kerosene oil, and their near infrared spectra were collected. The parameters of least squares support vector machine were optimized by particle swarm optimization algorithm. The optimal concentration quantitative models of three-component oil spills were established. The best regularization parameter C and kernel parameter σ of gasoline, diesel and kerosene model were 48.1418 and 0.1067, 53.2820 and 0.1095, 59.1689 and 0.1000 respectively. The decision coefficient R² of the prediction model were 0.9983, 0.9907 and 0.9942 respectively. RMSEP values were 0.0753, 0.1539 and 0.0789 respectively. For gasoline, diesel fuel and kerosene oil models, the mean value and variance value of predict absolute error were -0.0176±0.0636 μL/mL, -0.0084±0.1941 μL/mL, and 0.00338±0.0726 μL/mL respectively. The results showed that each component’s concentration of the oil spills samples could be detected by the NIR technology combined with PSO-LS-SVR regression method, the predict results were accurate and reliable, thus this method can provide effective means for the quantitative detection and analysis of complex marine oil spills.

In order to explore the laser propagation influence of thermal blooming effect of pipe flow and to analysis the influencing factors, scaling law theoretical analysis of the thermal blooming effects in pipe flow are carry out in detail based on the optical path difference caused by thermal blooming effects in pipe flow. Firstly, by solving the energy coupling equation of laser beam propagation, the temperature of the flow is obtained, and then the optical path difference caused by the thermal blooming is deduced. Through the analysis of the influence of pipe size, flow field and laser parameters on the optical path difference, energy scaling parameters Ne=nTαLPR2/(ρεCpπR02) and geometric scaling parameters Nc=νR2/(εL) of thermal blooming for the pipe flow are derived. Secondly, for the direct solution method, the energy coupled equations have analytic solutions only for the straight tube with Gauss beam. Considering the limitation of directly solving the coupled equations, the dimensionless analysis method is adopted, the analysis is also based on the change of optical path difference, same scaling parameters for the pipe flow thermal blooming are derived, which makes energy scaling parameters Ne and geometric scaling parameters Nc have good universality. The research results indicate that when the laser power and the laser beam diameter are changed, thermal blooming effects of the pipeline axial flow caused by optical path difference will not change, as long as you keep energy scaling parameters constant. When diameter or length of the pipe changes, just keep the geometric scaling parameters constant, the pipeline axial flow gas thermal blooming effects caused by optical path difference distribution will not change. That is to say, when the pipe size and laser parameters change, if keeping two scaling parameters with constant, the pipeline axial flow thermal blooming effects caused by the optical path difference will not change. Therefore, the energy scaling parameters and the geometric scaling parameters can really describe the gas thermal blooming effect in the axial pipe flow. These conclusions can give a good reference for the construction of the thermal blooming test system of laser system. Contrasted with the thermal blooming scaling parameters of the Bradley-Hermann distortion number ND and Fresnel number NF, which were derived based on the change of far field beam intensity distortion, the scaling parameters of pipe flow thermal blooming deduced from the optical path deference variation are very suitable for the optical system with short laser propagation distance, large Fresnel number and obviously changed optical path deference.

Heavy metal pollution in soils is one of the most critical problems in the global ecology and environment safety nowadays. Hyperspectral remote sensing and its application is capable of high speed, low cost, less risk and less damage, and provides a good method for detecting heavy metals in soil. This paper proposed a new idea of applying regression analysis of stepwise multiple regression between the spectral data and monitoring the amount of heavy metal Cr by sample points in soil for environmental protection. In the measurement, a FieldSpec HandHeld spectroradiometer is used to collect reflectance spectra of sample points over the wavelength range of 325-1075 nm. Then the spectral data measured by the spectroradiometer is preprocessed to reduced the influence of the external factors, and the preprocessed methods include first-order differential equation, second-order differential equation and continuum removal method. The algorithms of stepwise multiple regression are established accordingly, and the accuracy of each equation is tested. The results showed that the accuracy of first-order differential equation works best, which makes it feasible to predict the content of heavy metal Cr by using stepwise multiple regression.

This paper presents a simulation method of hyper-spectral dynamic scene and image sequence for hyper-spectral equipment evaluation and target detection algorithm. Because of high spectral resolution, strong band continuity, anti-interference and other advantages, in recent years, hyper-spectral imaging technology has been rapidly developed and is widely used in many areas such as optoelectronic target detection, military defense and remote sensing systems. Digital imaging simulation, as a crucial part of hardware in loop simulation, can be applied to testing and evaluation hyper-spectral imaging equipment with lower development cost and shorter development period. Meanwhile, visual simulation can produce a lot of original image data under various conditions for hyper-spectral image feature extraction and classification algorithm. Based on radiation physic model and material characteristic parameters this paper proposes a generation method of digital scene. By building multiple sensor models under different bands and different bandwidths, hyper-spectral scenes in visible, MWIR, LWIR band, with spectral resolution 0.01μm, 0.05μm and 0.1μm have been simulated in this paper. The final dynamic scenes have high real-time and realistic, with frequency up to 100 HZ. By means of saving all the scene gray data in the same viewpoint image sequence is obtained. The analysis results show whether in the infrared band or the visible band, the grayscale variations of simulated hyper-spectral images are consistent with the theoretical analysis results.

In terms of climate science, getting the accurate cloud particle sizes, shape and number distributions is necessary for searching the influence of cloud on the environment, radiative transfer, remote sensing measurements and understanding precipitation formation. Many methods and instruments have been developed to measure cloud particles, yet there is still restricted to one-dimensional or two-dimensional projections of particle positions, unable to get the three-dimensional information of the spatial distribution of particles. In-line holography is particularly useful for particles field measurements, because it can directly get the three-dimensional information of the particles and quickly access and storage holographic image. In this paper, the main work is using digital in-line holographic system to measure simulated cloud particles in the laboratory. For digital recording hologram reconstructing, we consider the image intensity in conjunction with the edge sharpness of the particles, to obtain an automatically selected threshold of each particle. Using the threshold, we can get a binary image to identify the particles and separate the particles from background, and then get the information such as the location, shape, particle size of particles. The experimental results show that the in-line digital holography can be used to detect the cloud particles, which can gain many parameters of the simulated cloud particles in the plane perpendicular to the optical axis, and can estimate volume parameters of the simulated cloud particles. This experiment is a basis for the further in situ detection of atmospheric cloud particles.

In order to realize real-time signal identification and processing of spatial transient light, the features and the energy of the captured target light signal are first described and quantitatively calculated. Considering that the transient light signal has random occurrence, a short duration and an evident beginning and ending, a photodiode detector based spatial transient light detection and processing system is proposed and designed in this paper. This system has a large field of view and is used to realize non-imaging energy detection of random, transient and weak point target under complex background of spatial environment. Weak signal extraction under strong background is difficult. In this paper, considering that the background signal changes slowly and the target signal changes quickly, filter is adopted for signal’s background subtraction. A variable speed sampling is realized by the way of sampling data points with a gradually increased interval. The two dilemmas that real-time processing of large amount of data and power consumption required by the large amount of data needed to be stored are solved. The test results with self-made simulative signal demonstrate the effectiveness of the design scheme. The practical system could be operated reliably. The detection and processing of the target signal under the strong sunlight background was realized. The results indicate that the system can realize real-time detection of target signal’s characteristic waveform and monitor the system working parameters. The prototype design could be used in a variety of engineering applications.

Aerosol light absorption plays an important role in the earth’s atmosphere direct and semi-direct radiate forcing, simultaneously, it also has a huge influence on the visibility impairment and laser engineering application. Although various methods have been developed for measuring aerosol light absorption, huge challenge still remains in precision, accuracy and temporal resolution. The main reason is that, as a part of aerosol light extinction, aerosol light absorption always generates synchronously with aerosol light scattering, and unfortunately aerosol light scattering is much stronger in most cases. Here, a novel photo-thermal interferometry is proposed only for aerosol absorption measurement without disturbance from aerosol scattering. The photo-thermal interferometry consists of a sinusoidal phase-modulating single mode fiber-optic interferometer. The thermal dissipation, caused by aerosol energy from photo-thermal conversion when irritated by pump laser through interferometer, is detected. This approach is completely insensitive to aerosol scattering, and the single mode fiber-optic interferometer is compact, low-cost and insensitive to the polarization shading. The theory of this technique is illustrated, followed by the basic structure of the sinusoidal phase-modulating fiber-optic interferometer and demodulation algorithms. Qualitative and quantitative analysis results show that the new photo-thermal interference is a potential approach for aerosol absorption detection and environmental pollution detection.

In the paper, an accurate and sensitive cavity attenuated phase shift spectroscopy (CAPS) system was used to monitor the atmospheric visibility coefficient in urban areas. The CAPS system, which detects the atmospheric visibility within a 10 nm bandpass centered at 532 nm, comprises a green LED with center wavelength in 532nm, a resonant optical cavity (36 cm length), a Photo Multiplier Tube detector and a lock in amplifier. The performance of the CAPS system was evaluated by measuring of the stability and response of the system. The minima (~0.06 Mm^-1) in the Allan plots show the optimum average time(~80s) for optimum detection performance of the CAPS system. The 2L/min flow rate, the CAPS system rise and fall response time is about 15 s, so as to realize the fast measurement of visibility. By comparing the forward scatter visibility meter measurement results, the CAPS system measurement results are verified reliably, and have high precision measurement. These figures indicate that this method has the potential to become one of the most sensitive on-line analytical techniques for atmospheric visibility detection.

Fresnel zone plate is a classic optical element that however can be used as the core spectral component of micro-spectrometer, which optical path volume can be decreased to 1mm³. After passing through the Fresnel zone plate, monochromatic lights of different wavelengths will expand to spectrum because of their different focal length. We designed eight kinds of linear half zone plates to get different focal lengths and spectral performance. In the process of fabrication, we used the electron beam lithography and inductively coupled plasma (ICP) etching to produce the Fresnel zone plates. Finally, performances of Fresnel zone plates were examined by beam-split experiments. After comparison, the simulation conclusion in reference that the number of bands of Fresnel zone plates will affect the final appearance of the spectrum was verified.

Mercury is known as a highly toxic metal, which will have a significant health hazard to the human body. To monitor the trace mercury pollution in air, the development of monitoring instruments has been conducted. In this paper the mercury analyzer is developed based on the cold atomic absorption spectrometry theory by exploiting the transverse Zeeman-Effect background correction technology. The experiments have been done to test the performance of the system. At the same time, the same experiments with RA-915 mercury analyzer have been done to compare with the results. First, zero gas was measured for an hour and high concentration mercury sample gas was measured for four days. The results of zero gas shows that the detection limit of the system is 2.19ng/m³ and the standard deviation is 0.73. The concentration fluctuation is within a tight range of ±1.5ng/m³. The results of high concentration sample gas are in good agreement with the results of RA-915, and the correlation coefficient is 0.95. Second, laboratory air was measured for 12 hours. The results compared with RA-915 are in good agreement and have the same variation trend. Additionally, the atmospheric mercury concentration near the non-ferrous metal smelter in Tongling city has been measured by the system and the RA-915. The measurement results from two analyzers have a good linear correlation with correlation coefficient of 0.98 and slope of 1.027. It indicates that the system has accurate background correction ability, low detection limit and is applicable to long-term air mercury on-line monitoring.

A microfluidic-based multi-angle laser scattering (MALS) sensor capable of acquiring scattering pattern of single particle is demonstrated. The size and relative refractive index (RI) of polystyrene (PS) microspheres were deduced with accuracies of 60 nm and 0.001 by analyzing the scattering patterns. We measured scattering patterns of waterborne parasites i.e., cryptosporidium parvum (c.parvum) and giardia lamblia (g.lamblia), and some other representative species in 1 L water within 1 hour, and the waterborne parasites were identified with accuracy better than 96% by classification of distinctive scattering patterns with a support-vector-machine (SVM) algorithm. The system provides a promising tool for label-free and rapid detection of waterborne parasites.

As one of the most interesting II–IV compound semiconductors, ZnO has large band gap (3.37 eV) and high excitonic binding energy (60 meV). Based on this, it has attracted a great deal of attention for applications in ultraviolet light-emitting devices (LED) and photodetectors. There are many preparation methods to prepare ZnO films, such as metal organic chemical vapor deposition (MOCVD), magnetron sputtering, vacuum thermal evaporation, and so on. Among them, there are many advantages on using magnetron sputtering to form ZnO thin films, such as good adhesion, good thickness uniformity, high density of films, so we take advantage of this method in our experiment. In this work, we present a simple, rapid and cost effective method to fabricate ordered periodic substrates by preparing single layer polystyrene microspheres masks, with 300, 600, 800and 1100 nm in diameters. Then the layer of zinc oxide thin films on the mask by RF magnetron sputtering technique have been deposited, and two-dimensional zinc oxide nano-array samples were obtained at last. Using this active plasmonic substrate, the optical properties of ZnO films on polystyrene microspheres template has been investigated.

In order to monitor carbon monoxide in industrial production, we developed a passive gas radiation measurement system based on Fourier transform infrared spectroscopy and carried out infrared radiation measurement experiment of carbon monoxide detection in simulated industrial production environment by this system. The principle, condition, device and data processing method of the experiment are introduced in this paper. In order to solve the problem of light path jitter in the actual industrial field, we simulated the noise in the industrial environment. We combine the advantages of MATHEMATICA software in the aspects of graph processing and symbolic computation to data processing to improve the signal noise ratio and noise suppression. Based on the HITRAN database, the nonlinear least square fitting method was used to calculate the concentration of the CO spectra before and after the data processing. By comparing the calculated concentration, the data processed by MATHEMATICA is reliable and necessary in the industrial production environment.

The data of current PM2.5 model forecasting greatly deviate from the measured concentration. In order to solve this problem, Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) are combined to build a rolling forecasting model. The important parameters (C and γ) of SVM are optimized by PSO. The data (from February to July in 2015), consisting of measured PM2.5 concentration, PM2.5 model forecasting concentration and five main model forecasting meteorological factors, are provided by Shanghai Meteorological Bureau in Pudong New Area. The rolling model is used to forecast hourly PM2.5 concentration in 12 hours in advance and the nighttime average concentration (mean value from 9 pm to next day 8 am) during the upcoming day. The training data and the optimal parameters of SVM model are different in every forecasting, that is to say, different models (dynamic models) are built in every forecasting. SVM model is compared with Radical Basis Function Neural Network (RBFNN), Multi-variable Linear Regression (MLR) and WRF-CHEM. Experimental results show that the proposed model improves the forecasting accuracy of hourly PM2.5 concentration in 12 hours in advance and nighttime average concentration during the upcoming day. SVM model performs better than MLR, RBFNN and WRF-CHEM. SVM model greatly improves the forecasting accuracy of PM2.5 concentration one hour in advance, according with the result concluded from previous research. The rolling forecasting model can be applied to the field of PM2.5 concentration forecasting, and can offer help to meteorological administration in PM2.5 concentration monitoring and forecasting.

A compact system based on mid-infrared quantum cascade laser (QCL) operated in room temperature was developed for the simultaneous monitoring of NO, NO₂ and NH₃ in the air. Laser beams of three QCLs with central wavelength located at 1900 cm-1, 1600 cm-1, 1103.4 cm-1 were coupled to pass through the 60m long gas cell together. With the technology of time division multiplexing, wavelength modulation spectroscopy (WMS) signals of three lasers can be detected at adjacent scan process. The real-time second harmonic analysis was implemented to achieve simultaneous detection of NO, NO₂ and NH₃. A minimum detection limit (MDL) of 0.2ppb for NO, 0.12ppb for NO₂ and 0.1ppb for NH₃ with an optimum integration time around 100 seconds can be achieved for this setup. An ambient monitoring of three gasses during 5 hours was performed to inspect the local air quality.

The convex grating is one of the key elements in Offner imaging spectrometers. In this paper the diffraction efficiency of the convex grating is investigated by using rigorous coupled-wave analysis (RCWA), which indicates that within the visible and near infrared broadband, the first-order diffraction efficiency can be over 40% through controlling the blaze angle of blazed convex grating. The blazed convex grating with the period of 6.17μm in the center, the blaze angle about 2.9 degree, the anti-blaze angle about 21 degree, and the ruled area – a convex substrate with its radius 36.31mm and aperture 23.6mm has been fabricated by holographic lithography - scan ion beam etching. Experimental measurements show that the first-order diffraction efficiency is more than 30%, and the first-order diffraction efficiency at blazed wavelength can reach 62%.

The pine wilt disease is a devastating disease of pine trees. In China, the first discoveries of the pine wilt disease on 1982 at Dr. Sun Yat-sen's Mausoleum in Nanjing. It occurred an area of 77000 hm² in 2005, More than 1540000 pine trees deaths in the year. Many districts of Chongqing in Three Gorges Reservoir have different degrees of pine wilt disease occurrence. It is a serious threat to the ecological environment of the reservoir area. Use unmanned airship to carry high spectrum remote sensing monitoring technology to develop the study on pine wood nematode disease early diagnosis and early warning and forecasting in this study. The hyper spectral data and the digital orthophoto map data of Fuling District Yongsheng Forestry had been achieved In September 2015. Using digital image processing technology to deal with the digital orthophoto map, the number of disease tree and its distribution is automatic identified. Hyper spectral remote sensing data is processed by the spectrum comparison algorithm, and the number and distribution of disease pine trees are also obtained. Two results are compared, the distribution area of disease pine trees are basically the same, indicating that using low air remote sensing technology to monitor the pine wood nematode distribution is successful. From the results we can see that the hyper spectral data analysis results more accurate and less affected by environmental factors than digital orthophoto map analysis results, and more environment variable can be extracted, so the hyper spectral data study is future development direction.

In order to reduce the calibration uncertainty of the reflectance-based method brought by the assumption of the aerosol model, the irradiance-based method, known as improved reflectance-based method, was proposed. The irradiance-based method is described in this paper. The radiometric calibration field campaign was performed at Dunhuang test site on 27 August, 2014. A hyperspectral irradiance meter (HSIM) developed by Anhui Institute of Optics and Fine Mechanics (AIOFM) was used to measure the diffuse-to-global spectral irradiance ratio. The irradiance-based method and the reflectance-based method were performed to calibrate the first four bands of Moderate Resolution Imaging Spectroradiometer (MODIS). The results of two methods were compared with result of MODIS on-board calibrator. The comparison shows that the result of irradiance-based method has a good consistency with on-board calibration and reflectance-based method results. The difference of calibration coefficients between irradiance-based and on-board method was less than 1.4%. Due to the limitations of the irradiance-based method, a clear sky and stable atmospheric condition is required for the entire half of the calibration day to provide the data necessary for the extrapolation of diffuse-to-global ratio in viewing direction. A study on the effects of aerosol mode assumption on the final apparent reflectance was performed on both the irradiance-based method and the reflectance-based method by selecting different aerosol modes to predict the apparent reflectance. The results show that aerosol mode assumption has a great effect on the reflectance-based method, however slight effect on the irradiance-based method.

Remote sensing image simulation plays an important role in spaceborne/airborne load demonstration and algorithm development. Hyperspectral imaging is valuable in marine monitoring, search and rescue. On the demand of spectral imaging of objects under the complex sea scene, physics based simulation method of spectral image of object under sea scene is proposed. On the development of an imaging simulation model considering object, background, atmosphere conditions, sensor, it is able to examine the influence of wind speed, atmosphere conditions and other environment factors change on spectral image quality under complex sea scene. Firstly, the sea scattering model is established based on the Philips sea spectral model, the rough surface scattering theory and the water volume scattering characteristics. The measured bi directional reflectance distribution function (BRDF) data of objects is fit to the statistical model. MODTRAN software is used to obtain solar illumination on the sea, sky brightness, the atmosphere transmittance from sea to sensor and atmosphere backscattered radiance, and Monte Carlo ray tracing method is used to calculate the sea surface object composite scattering and spectral image. Finally, the object spectrum is acquired by the space transformation, radiation degradation and adding the noise. The model connects the spectrum image with the environmental parameters, the object parameters, and the sensor parameters, which provide a tool for the load demonstration and algorithm development.

The multiplicative noise model for HS data cube was introduced in this paper. Based on this model, an algorithm was also developed to estimate the noise of the HS data in spatial and spectral domain. The comparison among the classical additive noise model, Poisson noise model and multiplicative was also discussed. The noise estimation experiments show that the multiplicative noise model is reasonable and suitable for HS data. The good performance of the NSR algorithms validated the effectiveness of the multiplicative noise model. The experiments also show that the multiplicative noise model have unique characteristics in information extraction of land cover. Based on the Multiplicative Noise model, we found that some natural objects like waters can be easily distinguished and extracted from the HS data using the NSR algorithm that we proposed in this study.

Hyperspectral remote sensing, featured by integrated images and spectra, is now a frontier of the remote sensing. Using meticulous spectra, hyperspectral remote sensing technology can depict spectral features of objects in detail and are capable of identifying objects rather than simply discriminating them. This study took the Baixianishan region in Gansu Province as an example, and CASI/SASI airborne hyperspectral data were utilized to extract and map alteration minerals by MTMF mapping method. Six hydrothermal alteration minerals were mapped, which contained limonite, sericite and epidote. In addition, we analyzed the types, combinations and distribution of the alteration minerals and divided three stages of hydrothermal activity. It is considered that the favorable ore-forming elements for gold deposits are middle Hercynian porphyraceous granite, fracture and veined distribution of sericite and limonite. The application of CASI/SASI airborne hyperspectral remote sensing data in the Baixianishan area has achieved ideal results, indicative of their wide application potential in the geological research.

Hyperspectral remote sensing technology has played a vital role in the identification and monitoring of oil spill events, and amount of spectral indices have been developed. In this paper, the applicability of six frequently-used indices is analyzed, and a combination of spectral indices in aids of support vector machine (SVM) algorithm is used to identify the oil slicks and corresponding thickness. The six spectral indices are spectral rotation (SR), spectral absorption depth (HI), band ratio of blue and green (BG), band ratio of BG and shortwave infrared index (BGN), 555nm and 645nm normalized by the blue band index (NB) and spectral slope (ND). The experimental study is conducted in the Gulf of Mexico oil spill zone, with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) hyperspectral imagery captured in May 17, 2010. The results show that SR index is the best in all six indices, which can effectively distinguish the thickness of the oil slick and identify it from seawater; HI index and ND index can obviously distinguish oil slick thickness; BG, BGN and NB are more suitable to identify oil slick from seawater. With the comparison among different kernel functions of SVM, the classify accuracy show that the polynomial and RBF kernel functions have the best effect on the separation of oil slick thickness and the relatively pure seawater. The applicability of spectral indices of oil slick and the method of oil film thickness identification will in aids of oil/gas exploration and oil spill monitoring.

The emissions of NO_X from Cement plant or Coal-fired power plant have serious pollution to the environment. In recent years, Selective Catalytic Reduction (SCR) is an effective means of reducing the emissions of NO_X by injecting ammonia into the combustion flue gas, which ideally reacts with the NO_X to produce harmless components (H₂O and N₂). The efficiency of SCR is determined by monitoring the ammonia slip of the flue exhaust outlet, excess ammonia injection can cause ammonia slip, which not only destroy the plant, but also increase the operating costs. In addition, ammonia is also pollution gases as NO_X. The flue gas at the measurement point is high temperature, vibrate and high particle density processes in Cement plant primarily, such harsh conditions coupled with the highly reactive nature of ammonia, so it is difficult to reliable extractive low level analysis. The paper describes an in-situ Tunable Diode Laser analyzer for measuring ammonia slip in the combustion flue gas after SCR in Cement Plant or Coal-fired power plant. A correlation filtering algorithm is developed to select high-quality spectral absorption signal, which improve the accuracy of concentration inversion of analyzer. The paper also includes field test data on an actual Cement plant all day, and we compare the ammonia slip and NO_X emissions of flue gas during actual production process, the results indicate that the measured values of the ammonia slip and NO_X emissions present a good correlation and comply with the principle of SCR.

With the progress of quantitative remote sensing, the acquisition of surface BRDF becomes more and more important. In order to improve the accuracy of the surface BRDF measurements, a VNIR-SWIR Bidirectional Reflectance Automatic Measurement System, which was developed by Hefei Institutes of Physical Science (HIPS), is introduced that allows in situ measurements of hyperspectral bidirectional reflectance data. Hyperspectral bidirectional reflectance distribution function data sets taken with the BRDF automatic measurement system nominally cover the spectral range between 390 and 2390 nm in 971 bands. In July 2007, September 2008, June 2011, we acquired a series of the BRDF data covered Dunhuang radiometric calibration test site in terms of the BRDF measurement system. We have not obtained such comprehensive and accurate data as they are, since 1990s when the site was built up. These data are applied to calibration for FY-2 and other satellites sensors. Field BRDF data of a Dunhuang site surface reveal a strong spectral variability. An anisotropy factor (ANIF), defined as the ratio between the directional reflectance and nadir reflectance over the hemisphere, is introduced as a surrogate measurement for the extent of spectral BRDF effects. The ANIF data show a very high correlation with the solar zenith angle due to multiple scattering effects over a desert site. Since surface geometry, multiple scattering, and BRDF effects are related, these findings may help to derive BRDF model parameters from the in-situ BRDF measurement remotely sensed hyperspectral data sets.

The starlight navigation is considered to be one of the most important methods for spacecraft navigation. Starlight simulation system is a high-precision system with large fields of view, designed to test the starlight navigation sensor performance on the ground. A complete hardware-in-the-loop simulation of the system has been built. The starlight simulator is made up of light source, light source controller, light filter, LCD, collimator and control computer. LCD is the key display component of the system, and is installed at the focal point of the collimator. For the LCD cannot emit light itself, so light source and light source power controller is specially designed for the brightness demanded by the LCD. Light filter is designed for the dark background which is also needed in the simulation.

For the safety of electronic equipment, a double-layer barrier of cylindrical plasma array was designed, and its protective performance to high-power microwave (HPM) were analyzed and the protective performance experiment was conducted. Combining the density distribution characteristic of the discharge plasma, the shielding effectiveness of the double-layer plasma on 6GHz HPM pulse was studied. The experiment results indicate that the protective effectiveness of two layers plasma array is better than that of one layer. Two layers plasma array can make the peak electric field of transmission waveform less than interference threshold of electronic equipment to achieve better protection effectiveness. Transmission attenuation of one layer and two layers plasma array to HPM can reach -6.6066dB and -24.9357dB. The results also show that for the existence of multiple reflection, even the plasma electron density is not high enough, it can realize a strong attenuation. The experiment results in this paper are of great significance in protecting against HPM and electromagnetic pulse.

Within administrative regions, urban built-up areas are vast stretches of constructed areas equipped with basic public facilit ies. Human act ivit ies most frequently take place within urban regions and the dynamic evolution of urbanization has caused profound variations in urban spatial structures. Conventional boundary extraction methods are complicated and require human intervention. This article innovatively proposes a vector method that combines a data-dimension compression index known as an Index-based Built -up Index (IBI) with aggregate analysis to extract vector boundaries of urban built-up areas automatically by setting a threshold value and the parameters for aggregate analysis. Datadimension compression technology is used to extract urban built-up areas using thematic bands (rather than original bands) to build indexes, which improves the precision of extraction. Areas ext racted by the methods above contains urban built-up areas, rural built-up areas, independent houses and fully bare areas. Aggregate analysis aggregates a certain range of non-adjacent plots into a new polygon section. This method has made it easy to analyze the spatial expansion of Wuhan city from 2003 to 2013. This method avoids cumbersome process es of outlining vector boundaries by artificial visual interpretation with a better working efficiency and reduced costs than other methods, which cannot accurately determine vector boundaries to an accurate degree by manual vector quantizat ion without depending on other data or expert knowledge. Compared with t raditional boundary extraction methods, this vector method is more efficient, precise, objective, and exquisite.

Space-borne high signal-to-noise ratio (SNR) and high spectral resolution spectral detection system with high detection accuracy (1-4ppm) is demonstrated under the application background of the detection of atmospheric carbon dioxide as the main component of greenhouse gases. According to greenhouse gas concentrations detection accuracy requirements and simulation of different spectral absorption spectrum of carbon dioxide, the reasonable spectral channel center wavelength, spectral bandwidth and spectral resolution is determined of the high spectral resolution carbon dioxide remote sensing system. Grating spectral imaging system using large area diffractive grating spectral as a core splitting element is to achieve fine spectrum splitting. By the application of large area array detector push-broom mode, the hyperspectral greenhouse gas detection system is developed with the spectrum center wavelength of 0.76um, 1.61um and 2.06um, spectral resolution indicators better than 0.047nm, 0.142nm and 0.182nm actually. The system components and working principle are described. Important parts involved in the system design such as spectral imaging system, large-array CCD visible-light detector, large-array HgCdTe infrared detectors, high SNR and low temperature drift imaging electronics, etc. are discussed. SNR indicators of three spectral ranges are estimated based on system parameters, in order to analyzing realizability of high detection accuracy of XCO₂. The system performances are tested by taking fine spectral calibration and radiometric calibration methods in the laboratory. Spectral calibration results showed that: three spectral channels mean spectral resolutions of hyperspectral detection of greenhouse gases are better than 0.042 nm, 0.128nm and 0.17nm, three spectral channels average SNRs are up to 53dB, 48dB and 45dB respectively under the typical operating conditions of system. Development of this system successfully filled greenhouse gas detection systems onboard the blank in China.

Hyperspectral LiDAR using supercontinuum laser as light source, applying spectroscopic technology gets backscattered reflectance of different wavelengths, and can acquire both the geometry and spectral information on the target. Due to the development of the photoelectric sensor, hyperspectral LiDAR has fewer spectral channels, which limits its application in physical properties detection. To solve this problem, this paper proposes a new method based on the micro mirror array. By blaze grating, the supercontinuum laser is grating into monochromatic light in space, first projected to the micro mirror array, by controlling the micro mirror array flip, specific spectrum and reflection to corresponding photoelectric sensor channels, improve the spectral resolution. The micro mirror array photoelectric sensor resolution is much higher than the number of channels, through this method, can greatly improve the spectral resolution. In this paper, based on the micro mirror array, the simulation design is carried out and the feasibility of the method is verified by experiments. The simulation and experimental results show that the spectral resolution can be improved greatly by controlling the turning of the micro mirror.

This paper presents a simulation method of hyperspectral dynamic scene and image sequence for hyperspectral equipment evaluation and target detection algorithm. Because of high spectral resolution, strong band continuity, anti-interference and other advantages, in recent years, hyperspectral imaging technology has been rapidly developed and is widely used in many areas such as optoelectronic target detection, military defense and remote sensing systems. Digital imaging simulation, as a crucial part of hardware in loop simulation, can be applied to testing and evaluation hyperspectral imaging equipment with lower development cost and shorter development period. Meanwhile, visual simulation can produce a lot of original image data under various conditions for hyperspectral image feature extraction and classification algorithm. Based on radiation physic model and material characteristic parameters this paper proposes a generation method of digital scene. By building multiple sensor models under different bands and different bandwidths, hyperspectral scenes in visible, MWIR, LWIR band, with spectral resolution 0.01μm, 0.05μm and 0.1μm have been simulated in this paper. The final dynamic scenes have high real-time and realistic, with frequency up to 100 HZ. By means of saving all the scene gray data in the same viewpoint image sequence is obtained. The analysis results show whether in the infrared band or the visible band, the grayscale variations of simulated hyperspectral images are consistent with the theoretical analysis results.

The circle-plus-line trajectory satisfies the exact reconstruction data sufficiency condition, which can be applied in C-arm X-ray Computed Tomography (CT) system to increase reconstruction image quality in a large cone angle. The m-line reconstruction algorithm is adopted for this trajectory. The selection of the direction of m-lines is quite flexible and the m-line algorithm needs less data for accurate reconstruction compared with FDK-type algorithms. However, the computation complexity of the algorithm is very large to obtain efficient serial processing calculations. The reconstruction speed has become an important issue which limits its practical applications. Therefore, the acceleration of the algorithm has great meanings. Compared with other hardware accelerations, the graphics processing unit (GPU) has become the mainstream in the CT image reconstruction. GPU acceleration has achieved a better acceleration effect in FDK-type algorithms. But the implementation of the m-line algorithm’s acceleration for the circle-plus-line trajectory is different from the FDK algorithm. The parallelism of the circular-plus-line algorithm needs to be analyzed to design the appropriate acceleration strategy. The implementation can be divided into the following steps. First, selecting m-lines to cover the entire object to be rebuilt; second, calculating differentiated back projection of the point on the m-lines; third, performing Hilbert filtering along the m-line direction; finally, the m-line reconstruction results need to be three-dimensional-resembled and then obtain the Cartesian coordinate reconstruction results. In this paper, we design the reasonable GPU acceleration strategies for each step to improve the reconstruction speed as much as possible. The main contribution is to design an appropriate acceleration strategy for the circle-plus-line trajectory m-line reconstruction algorithm. Sheep-Logan phantom is used to simulate the experiment on a single K20 GPU. The development environment trajectory, using CPU and the paper’s GPU acceleration strategy, respectively. The experimental results show considerable reconstruction image quality, and the reconstruction acceleration ratio can reach 620 times.

Camera calibration is one of the indispensable processes to obtain 3D depth information from 2D images in the field of computer vision. Camera self-calibration is more convenient and flexible, especially in the application of large depth of fields, wide fields of view, and scene conversion, as well as other occasions like zooms. In this paper, two selfcalibration methods respectively based on two vanishing points and homography are studied, and finally realizing the image mosaic based on self-calibration of the camera purely rotating around optical center. The geometric characteristic of disappear points formed by two groups of orthogonal parallel lines is applied to self-calibration based on two vanishing points. By using the vectors’ orthogonal properties of connection optical centers and the vanishing points, the constraint equations on the camera intrinsic parameters are established. By this method, four internal parameters of the camera can be solved though only four images taked from different viewpoints in a scene. Compared with the other selfcalibration based on homography, the method based on two vanishing points has more convenient calibration process and simple algorithm. To check the quality of the self-calibration, we create a spherical mosaic of the images that were used for the self-calibration based on homography. Compared with the experimental results of two methods respectively based on calibration plate and self-calibration method using machine vision software Halcon, the practicability and effectiveness of self-calibration respectively based on two vanishing points and homography is verified.