This PDF file contains the front matter associated with SPIE Proceedings Volume 8002, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

Urban environment is extremely complex due to a multitude of features with different heights and structures. Traditional methods available to extract information regarding the buildings by using optical remote sensing images are highly labor-intensive and time-consuming. This paper developed a new method to detect building outlines based on height and intensity information of Airborne LiDAR data. Texture, relative height and intensity characteristics were first extracted from the LiDAR point cloud. Then, Support Vector Data Description was used to detect buildings with training knowledge. Finally, building outlines were obtained after data post process including small region removal, raster to vector conversion and so on. Experiments show the method proposed in this study is reliable and could be widely used in other urban areas.

A novel wireless power driving and controlling system is proposed, which is based on the electrical driving and controlling characteristic of liquid crystal micro-lens array with tunable focal length. The system consists of two isolated circular copper stacked coils with circular cross section. Both of coils are air-cored. The big one, used to generate electromagnetic field, has the wire diameter of ~1mm, and the coil diameter of ~38mm and the height of 10mm. By contrast, the small one, used to produce electromotive force by induction effect, is ~1mm, ~13mm and ~18mm, respectively. The varying electromotive force is generated in the small coil by the variable intensity of magnetic field resulting from changing the time of current through the big coil. The agreement of experimental results with the theoretical analysis proves that the electromotive force can be continuously changed from 0 to 30Vrms (root mean square), when the duration of current through the big coil is reasonablely selected. It is very important that the clear image can be got and the image quality of liquid crystal micro-lens array can be comparable to that of conventional optical micro-lens array.

The performance of HgCdTe infrared photoconductors is strongly dependent on the semiconductor surface conditions. In this paper, the effect of fixed charge density(Q_ox) due to passivation on the responsivity of HgCdTe photoconductive detectors is analysed both theoretically and experimentally. A profile responsivity model is used here for calculation, which mainly includes the contribution of minority carrier lifetime and the shunt resistance resulting from the accumulation layer at the surface. In this model, the profiles of surface majority carrier concentration and surface mobility are taken into consideration. A gate-controlled photoconductor structure is designed and fabricated to investigate surface effects on HgCdTe infrared photoconductive detectors. And it is used to evaluate and optimize surface passivation layers. Minority carrier lifetime, resistance and responsivity of the device have been measured as a function of the gate potential in this structure. The measured variations have shown a reasonable agreement with our model. It is predicted that the optimization of surface fixed charges at the MCT-passivant interface can bring a great improvement in the responsivity of photoconductive detectors.

In liquid zoom lens, the accurate fixed-focus location needs the accurate calculation of focal length. That is to say, the accurate calculation of surface curvature must be obtained. Therefore, the research of their relation in liquid zoom lens becomes extremely important which has a directly theoretical instruction for the design of liquid zoom lenses. In our paper, the relation is studied. A manual liquid lens is reported which has upper and nether components with specificaton Φ12×5mm. The zoom lens is obtained by the upper component circumvolving down into the nether components.

The mechanism of image motion of Airborne Digital Camera was analyzed and a method of external swing forward motion compensation was proposed in this paper. The forward motion compensation for external swing was achieved through the design of a compensation device to control the rotation of the camera system. In order to simulate the actual motion effect at the most under the laboratory condition, the Stepper Motor Controller (SC) stepper motor controller was designed as the simulation installment to control the guide rail movement. Using this device, compensation speeds and compensation could be controlled, and hence the evaluation of compensation effect could be conducted by image sharpness. The experimental results of the forward motion compensation, show that the compensated remain image motion was less than 2μm (1/3 pixel), illustrating the feasibility and effectiveness of the method proposed in this paper.

Range gating is a kind of effective method which can eliminate backscattered light and increase the SNR of the laser range gated (LRG) imaging system. In the paper, we describe a computer model that was developed to simulate the imaging data of the LRG system. It is used to help design LRG system, and also acquire LRG imaging data used for further research on surveillance, city topography, combat identification, and other applications. The experiments are made in the condition of varied pulse energy, range gate width, atmospheric transmition, then the simulation result is analyzed and the related conclusion is obtained. It demonstrates that the simulating image data is suit for further study on many areas.

Look-up table can accelerate the atmospheric radiation transmission simulation in remote sensing imaging simulation effectively. Because of easily using and convenient for re-development of 6S, the construction of look-up table based on 6S was discussed in this paper. After the introduction of 6S, the parameters of 6S which should be including in the look-up table and have the mainly relativity in the processing of atmospheric radiation transmission calculation were analyzed. Then the construction scheme was designed, and look-up table was generated based on 6S executable program. And the validity of atmospheric radiation transmission calculation using look-up table instead of 6S model was verified. The experiment shows that the luminance of target in every geometric conditions which interpolated by look-up table has nice precision, and the look-up table construction scheme discussed in this paper can meet the needs of anticipation.

In this paper, a new approach is proposed to simulate images impacted by the Aero-Optical Effect (AOE) of High-Speed Flow Field. We first study the mechanism of the Aero-Optical Effect of High-Speed Flow Field, and its influence on the imaging system of the aircrafts. After reviewing existing methods of simulating blurred images impacted by the AOE, we show a novel method of simulating uneven blurred images which makes a great contrast to the traditional algorithms. Experimental results indicate that the blurred images generated by our methods are much closer to authentic situations than the previous results.

In single Doppler radar wind retrieval technique there are three methods proposed in recently years, which is VAP, VPP and SVVP method. Three methods were employed to retrieve wind field with the purpose to find out which one is more suitable in practice according their accuracy by using synthetic data and real data. When tests applied with uniform data, the orders of magnitude for relative error of radial velocity is 10^-13 for SVVP, much lower than that of VAP and VPP which both are 10^-4. Furthermore, the results under condition of wind direction unchanged and wind velocity varied linearity with altitude are 10^-8, 10^-3, 10^-3 for SVVP, VPP and VAP, respectively. In real wind field of typhoon "Pearl", the authenticity of surrounding wind and wind shear retrieved by SVVP and VPP is better than VAP, the deviation of simulation would be increased at some points missing data. Relative errors of radial velocity achieved by VAP, VPP and SVVP are 7.02, 5.78 and 3.1 respectively. It indicated that SVVP method's performance is better than that of other two and suitable in practical applications.

We develop an analytical expression for probability density function of speckle intensity on receiving aperture and express integral speckle intensity variance when receive aperture area is s. Analytical and numerical results of speckle number are given for rectangular and circular diffuser apertures. Laser radar intensity image are simulated in different speckle numbers on receive aperture. The results shows that the image is becoming more and more clearly as speckle numbers increase by the increasing of receiving aperture and decrease of the light beam waist.

This paper proposes a theoretical method based on physical model to calculate infrared (IR) radiation spectrum. IR physics establishes that emission abilities of objects are measured by emissivity, while the emissivity of objects is based on the emissivity of a blackbody. Planck formula shows how to calculate the spectral energy density of a blackbody, and IR radiation spectrum of a sample can be easily obtained if its emissivity is known. Kirchhoff's law shows the emissivity of an object is equal to its absorptance at the same conditions. By calculating the absorption coefficient using line-by-line model, the absorptance can be achieved through the relationship between them. The performance data of the technique is from public databases.

This paper concerned HgCdTe (MCT) infrared photoconductor detectors with high operating temperature. The near room temperature operations of detectors had advantages of light weight, less cost and convenient usage. The selection of material could greatly reduce cost caused by low performance detectors, with usage of unsuitable wafers. Many characteristic parameters were tested before the production period, material were chosen to produce the high yield detectors. Using these parameters for model construction and hypothesis testing, we could efficiently select the material most suitable for high performance detectors. Using the methods of statistical inferences, the product's fabrication management and maintenance abilities could also be greatly improved. In this paper, data of detectors were collected from detectors used in railway observation. The MCT detector was photoconductive with an 800μm× 800μm active area, which responses to radiation from 3μm to 5μm, the detector material was grown by the Bridgeman technique. The hypothesis test was a measure of how close the performance of selected fiber was to the empirical distribution, the Kolmogrov-Smirnov test, Anderson-Darling test and Chi-square goodness-of-fit test were used in this paper. The selection of material had a statistical credibility if the null hypothesis was not rejected. The reliability of detectors could also be improved with the selection of detector parameters.

A research for developing a kind of smart spectral imaging detection technique based on the electrically tunable liquidcrystal (LC) FP structure is launched. It has some advantages of low cost, highly compact integration, perfuming wavelength selection without moving any micro-mirror of FP device, and the higher reliability and stability. The controlling system for hyper-spectral imaging array based on LC-FP device includes mainly a MSP430F5438 as its core. Considering the characteristics of LC-FP device, the controlling system can provide a driving signal of 1-10 kHz and 0- 30Vrms for the device in a static driving mode. This paper introduces the hardware designing of the control system in detail. It presents an overall hardware solutions including: (1) the MSP430 controlling circuit, and (2) the operational amplifier circuit, and (3) the power supply circuit, and (4) the AD conversion circuit. The techniques for the realization of special high speed digital circuits, which is necessary for the PCB employed, is also discussed.

This paper designs a sub-wavelength metal polarization gratings array composing two orthogonal micro polarization gratings as one unit. Effects of different metal materials and grating profile on the TM and TE polarization transmittance as well as the extinction ratio are analyzed by the Finite Difference Time Domain (FDTD) method. Based on the requirement of the visible light polarization imaging and the resolution of the holographic lithography, we obtain the best parameters for the grating: period is 250nm, Al thickness is 260nm, and duty cycle is 0.4, the transmittance is higher than 45% and extinction ratio is higher than 100. Then, the sub-wavelength metal polarization gratings have been fabricated by two methods: the holographic - ion beam etching - Al oblique deposition or the holographic - reactive ion etch (RIE) - ion beam etching. Preliminary results indicate the polarization information has been obtained. A prototype metal polarization gratings array will be fabricated in late 2011.

To build a suit of integrated ship model database, a new method of constructing ship three-dimensional models is proposed which partition ship into basic structure and special structure of ship body. According to the distributing of ship structure, segmenting the whole ship area, and then marking off the basic hierarchies of practice ship, finally disassembling to basic unit structure, thus setting up corresponding tree hierarchies of ships. The modeling procedures of build ship 3D model are presented, Simulation results of three-dimensional ship models have gained vivid solid effect.

In this paper, a novel super resolution (SR) method for remote sensing images based on compressive sensing (CS), structure similarity and dictionary learning is proposed. The basic idea is to find a dictionary which can represent the high resolution (HR) image patches in a sparse way. The extra information coming from the similar structures which often exist in remote sensing images can be learned into the dictionary, so we can get the reconstructed HR image through the dictionary in the CS frame due to the redundance in the image which has a sparse form in the dictionary. We use K-SVD algorithm to find the dictionary and OMP method to reveal the sparse coding coefficient's location and value. The difference between our method and the previous sample-based SR method is that we only use low-resolution image and the interpolation image from itself rather than other HR images. Experiments on both optical and laser remote sensing images show that our method is better than the original CS-based method in terms of not only the effect but also the running time.

Detecting vegetation change is critical for earth system and sustainability science. The existing methods, however, show several limitations, including inevitable selection of imagery acquisition dates, affection from vegetation related noise on temporal trajectory analysis, and assumptions due to vegetation classification model. This paper presents a multitemporal phenological frequency analysis over a relatively short period (MTPFA-SP) methodology to detect vegetation changes. This MTPFA-SP methodology bases on the amplitude components of fast Fourier transforming (FFT) and is implemented with two steps. First, NDVI time series over two periods are transformed with FFT into frequency domain, separately. Second, amplitude components with phenological information from Step 1 are selected for further change comparison. In this methodology, component selection shows physical meanings of natural vegetation process in frequency domain. Comparisons among those selected components help enhance the ability to rapidly detect vegetation changes. To validate this MTPFA-SP methodology, we detect changes between two periods (2001-2005 and 2006-2010) in the eastern Tibet Plateau area and make two kinds of assessments. The first is for a larger scale, including statistic analysis of altitudinal zonality and latitudinal zonality. The second assessment is for rapid detection of vegetation change location. Landsat TM image were employed to validate the result.

A special software is constructed effectively for reconstructing the fine phase distribution of the diffracted Gaussian laser beams in the terahertz frequency range, according to common diffraction theory. The fine surface microrelief patterns of the elements, which originate from the simple patterns in photomask and further etched onto the surface of {100}- oriented silicon wafer by a low cost and rapid method, are created by the software above according to the phase distribution designed. Being different with the traditional silicon diffractive lenses fabricated by multiple level processes, the elements produced by the method introduced by us can transfer common Gaussian beams into desired images through created fine patterns over the surface of the elements. Two typical type of diffractive elements, which are used to transform common Gaussian laser beams in terahertz frequency into highly focused spot or so-called common focus, and the desired figure of the "umber one", are designed and fabricated. For testing the element, the LASER SIEIR 50 of Coherent Company is used to generate common Gaussian laser beams (the diameter of the beams is ~10mm), and the PYROCAM THERE of Spiricon Company is also used to display the images acquired. Experimental results show that the elements can be used to form needed light fields and expected images, respectively.

In this paper, we propose a joint source-channel coding (JSCC) scheme for image transmission over wireless channel. In the scheme, fountain codes are integrated into bit-plane coding for channel coding. Compared to traditional erasure codes for error correcting, such as Reed-Solomon codes, fountain codes are rateless and can generate sufficient symbols on the fly. Two schemes, the EEP (Equal Error Protection) scheme and the UEP (Unequal Error Protection) scheme are described in the paper. Furthermore, the UEP scheme performs better than the EEP scheme. The proposed scheme not only can adaptively adjust the length of fountain codes according to channel loss rate but also reconstruct image even on bad channel.

With the deterioration of ecological environment, rare plants on the earth are decreasing rapidly, so there is an urgent need for the study on sophisticated vegetation classification. Hyperspectral data have great potential in sophisticated classification. FISS(Field Imaging Spectrometer System) is a newly developed system, and pixels of FISS could be considered as pure pixels with high spatial and spectral resolution, which makes FISS a perfect option on the study of methodology. This study aims to evaluate different methods based on FISS data and find out the best one of sophisticated vegetation classification. The methods are as follows: Maximum Likelihood (ML), Spectral Angle Mapping (SAM), Artificial Neural Net (ANN), Support Vector Machine (SVM) and Composite Kernel Support Vector Machine (C-SVM). Firstly, segmented principal components transformation is adopted for spectral dimensionality reduction, and all bands are divided into 2 subsets according to the correlation matrix. Secondly, 16 principal components are kept. After that, 5 methods mentioned above are tested. The Overall Accuracy and Kappa coefficient of C-SVM, SVM and ANN are higher than 90%, and C-SVM obtains the highest accuracy, which is consistent with visual interpretation. The result shows that C-SVM, SVM and ANN are more suitable for sophisticated vegetation classification of hyperspectral data, and C-SVM is the best option.

This paper firstly improves D.L.Plillips's representation about image restoration and then points out that image restoration is just a 'partial ill-posed' problem rather than a 'total ill-posed' problem---amplitude restoration is ill-posed but phase restoration is well-posed. Basing on the viewpoints, the paper proposes a restoration method, which cuts down phase-pollution caused by traditional regularization methods, that amplitude restoration is realized by regularization and phase restoration is achieved by algebraic method. Experimental results indicate that the proposed method performs well. It can efficiently restore image phase and elaborately preserve image details.

The manifold learning theory is firstly used to transform the hyperspectral images into a low-dimension feature spaces. The reconstruction error is computed to get discriminative information. Then a structured matched subspace detector is developed. This method can effectively avoid the contamination by targets and spectral anomalies to backgrounds subspace and detect sub-pixel targets with better performance than traditional methods.

Infrared focal plane arrays (IRFPA) suffer from inherent low frequency and fixed patter noised (FPN). They are thus limited by their inability to calibrate out individual detector variations including detector dark current (offset) and responsivity (gain). To achieve high quality infrared image by mitigating the FPN of IRFPAs, we have developed a novel non-uniformity correction (NUC) method based on read-out integrated circuit (ROIC). The offset and gain correction coefficients can be calculated by function fitting for the linear relationship between the detector's output and a reference voltage in ROIC. We tested the purposed method using an infrared imaging system using the ULIS 03 19 1 detector with real nonuniformity. A set of 384*288 infrared images with 12 bits was collected to evaluate the performance. With the experiments, the non-uniformity was greatly eliminated. We also used the universe non-uniformity (NU) parameter to estimate the performance. The calculated NU parameters with the two-point calibration (TPC) and the purposed method imply that the purposed method has almost as good performance as TPC.

The deviation coefficient (Cv) and the arithmetic-geometric mean ratio (A/G) are two kinds of different measurement methods of SAR image heterogeneity. They are widely used for SAR image processing. This paper analyzes the Kuan filtering algorithm based on the deviation coefficient and puts forward the Kuan filtering algorithm based on the arithmetic-geometric mean ratio. And meanwhile, it analyzes and compares the two types of adaptive speckle noise filters through the experiment.

Ultrasonic image formed by Phased Array Ultrasonic probes placed on wheel tread is deformed. Especially for ultrasonic image around area of wheel disk hole, because that area has a complex construction and a long ultrasonic path. According to probes layout and beam angle, correction algorithms are designed to revise deformed ultrasonic images. Tests on CRH reference wheels with manmade defects and tests on CRH wheels on train shows that the correction algorithms proposed can correct all types of CRH wheel's ultrasonic images, and the calibration can meet the requirement of field application.

A fractal dimension feature analysis method in spectrum domain for hyper spectrum image is proposed for agriculture crops classification. Firstly, a fractal dimension calculation algorithm in spectrum domain is presented together with the fast fractal dimension value calculation algorithm using the step measurement method. Secondly, the hyper spectrum image classification algorithm and flowchart is presented based on fractal dimension feature analysis in spectrum domain. Finally, the experiment result of the agricultural crops classification with FCL1 hyper spectrum image set with the proposed method and SAM (spectral angle mapper). The experiment results show it can obtain better classification result than the traditional SAM feature analysis which can fulfill use the spectrum information of hyper spectrum image to realize precision agricultural crops classification.

Good polarization SAR speckle reduction effect is very helpful to follow-up treatment for such as Polarization SAR image classification and interpretation. Based on analysis of the existing polarization SAR image filtering algorithm, an algorithm of PolSAR speckle reduction was proposed to maintain the relative phase information with SAR image speckle reduction of the total variation of partial differential equations (TV-PDE). First, three polarization combinations which could be represented by three color channels were got from Polarization SAR data with Pauli decomposition. Then, the color channels were operated with TV-PDE speckle reduction algorithm. The experiment results showed that the algorithm is effective and of good performance in the filtering and edge preservation. Comparison was made with other speckle filtering algorithms taking speckle index as the evaluation criteria.

Kaarna et al. [pro. Scand. Cof. Image Analysis, SCIA 2003, pages 320-327] proposed a watermarking method based on the three dimensional wavelet transform for spectral images. kaarna et al [J. Imaging SCI. Technol. 52, pages 30502-1 - 30502-18, 2008] reported that the robustness of the watermarking method to different illumination conditions. The spectral image database provider stores the reflectance or radiance spectra of the images. Depending on the client's requirements, the effects from illumination can be added to the spectra, i.e., the viewing conditions change the perceived color of the spectrum. External illumination can be compensated through convoluting the spectra of the image with the spectrum of the illuminant. In this paper, a hybrid watermarking method based on the three-dimensional wavelet transform and singular value decomposition is proposed. The proposed method is compared with the 3D-DWT method of kaarna et al in the cases both with and without effect of different illumination conditions. Experiments were performed on a spectral image of natural scenes. Inlab2 was selected. The color reproduction is done using CIE XYZ basis function with D65 light model. Inlab2 image have the following dimensions: 256x256 pixels, and 31 spectral components per each pixel. Images were captured by a CCD (charge coupled device) camera in a 400-700 nm wavelength range at 10 nm intervals. The image selected was taken indoor (in a controlled environment, i.e. dark-lab or glass-house). The performance of the proposed technique is compared with the work of kaarna et al against different illumination conditions and attacks including median and mean filtering, lossy compression. The experiments indicate, the proposed method outperforms the work of kaarna et al.

This study was carried out to develop a hyperspectral imaging system in the near infrared (NIR) region (900-1700 nm) to diagnose cucumber downy mildew. Hyperspectral images were acquired from each diseased cucumber leaf samples with downy mildew and then their spectral data were extracted. Spectral data were analyzed using principal component analysis (PCA) to reduce the high dimensionality of the data and for selecting some important wavelengths. Out of 256 wavelengths, only two wavelengths (1426 and 1626nm) of first PC were selected as the optimum wavelengths for the diagnosis of cucumber downy mildew. The data analysis showed that it is possible to diagnose cucumber downy mildew with few numbers of wavelengths on the basis of their statistical image features and histogram features. The results revealed the potentiality of NIR hyperspectral imaging as an objective and non-destructive method for the authentication and diagnosis of cucumber downy mildew.

Edge and detail of infrared image are blurry or loss after denoising with the threshold shrinkage arithmetic. A new adaptive denoising and enhancing algorithm with detail enhancement based on a new Contourlet Transform with Sharp Frequency Localization(CT-SFL) is proposed to preserve the edge better. CT-SFL has the characteristic of well-localized in the frequency domain compared with the original contourlet. Firstly, CT-SFL, instead of the original contourlet, is employed as the multiscale decompositon to decompose the infrared image into subbands. Secondly, the hierarchical adaptive denoising threshold of new Contourlet coefficient is estimated respectively by each location from different scale and directional subband, the noisy image is denoising with soft threshold related to the transform scale and direction, then the denosing image is enhanced by taking decomposable scale and directional energy into account with intrasubband and interscale dependencies. Thirdly, inverse CT-SFL is used to reconstruct the denoising and enhancing image. Finally, in order to reduce significant amount of aliasing components which are located far away from the desired support because of the new Contourlet Transform, cycle spinning is accomplished to the whole denoising and enhancement process to overcome the lack of translation invariance property and suppress pseddo-Gibbs phenomena around singularities of denoising image. Numerical experiments on infrared noisy image show that the proposed novel algorithm can significantly outperform some arithmetics based on contourlet like 3 sigma, VisuShrink and Bayes Shrinkage in all kinds of noise spectral density both in terms of PSNR(by several dB) and in visual quality, which can enhance image's detail and stretch its contrast with nearly similar computational complexity.

This paper proposes an improved blind deconvolution algorithm, which adopts maximum likelihood method to find the most similar estimation of the PSF and object with Poisson-based probability model. The algorithm integrates Cauchy probability distribution model into the estimation of the PSF under the condition of low SNR, uses the characteristic of short-exposure image sequence that the adjacent images have similar PSF to get restored image with frames as few as possible. The experimental results show that this method is robust with high ability of resisting noise in the restoration of turbulence-degraded images.

A variational image adaptive denoising model based on human visual system is proposed by introducing control parameter p which can determine the diffusion intensity to Total Variation (TV) model. The model can adaptively select the value of parameter p according to human visual system noise visibility value of each pixel which makes diffusion intensity close to edges smaller than those far away from edges. For this method is more consistent with human perception, human eyes can perceive the improvement of image quality intuitively. Numerical experiments show that the proposed method can overcome staircase effect, remove the noise while preserving significant image details and better performance has been achieved.

In this paper, snow disaster assessment model based on analytical hierarchy process, snow range extraction model and snow depth inversion model have been established. The established disaster assessment models and MODIS data have been employed to evaluate snow situation in north Xinjiang from November 2005 to January 2006. The results show that the proposed model can effectively assess snow disaster situation and be used in disaster situation management.

Determination of land cover spatial distribution and change scientifically and accurately have a great significance for monitoring the regional ecological environment and the impact of human activities. This paper proposed a method basing on the waveform characteristics of spectral for mapping large area using TM data simply, quickly and accurately. In the experiments, we segment the image to object and order the object value of the TM5 1~7 except the thermal infrared band and each order result given the only value. In the next, using manual methods assigned the order to get the typical surface feature such as water, soil, vegetation. Classification using spectral waveform characteristics can effectively avoid the TM image classification error, due to spectral differences by accessing time and spatial, spectrum differences on same object and spectral mixing .This method can map the large area of typical surface is simply, fast and accurate, and support on theoretical basis.

This paper presents a method to estimate the soil moisture using ENVISAT ASAR data in the Three Gorges Reservoir area in China. Firstly, this study introduces a semi-empirical model for bare surface scattering and a water-cloud model for the elimination of the impact of the vegetation cover. Secondly, a new combined roughness parameter is introduced to describe the roughness of soil surface. Thirdly, we analyze the relationship between soil roughness and radar backscattering coefficient and that between soil moisture content and backscattering coefficient respectively. Then the soil moisture inversion algorithm is achieved through programming. Finally, an evident logarithmic relationship between radar backscattering coefficient and soil moisture is presented. In conclusion, the experiments prove that the method is fast, efficient and widely applicable.

As the X-band marine radar often suffers from interference of electromagnetic waves of the same frequency transmitted by radars in its vicinity, the acquired images frequently contain co-channel interference noise. The noise degrades the quality of the marine radar images and is unfavorable to the processing and interpretation of the marine radar images. To suppress the noise in marine radar images, a novel method based on pulse-pulse correlation is proposed. This method includes three steps: threshold segmentation, noise extraction and noise fixing. In the threshold segmentation step, the threshold T is calculated based on the K distribution sea clutter model. In the noise extraction step, a 3×3 window is applied. By using the window, the pixels of noise can be extracted, and at the same time the pixels of non-noise can be discarded. In the noise fixing step, the strategy of piecewise interpolation is applied. At the region near to the image center, the triangulation with linear interpolation algorithm is applied; at the region far from the image center, the nearest neighbor algorithm is applied. The real X band marine radar image was used to test the performance of the proposed method. The obtained results show that the proposed method is able to reduce the co-channel interference noise from the marine radar images significantly and keep the information of objects in the images such as ships and islands. Besides, the proposed method can be fast in speed of operation.

The remote sensing image recorded the ground object spectral responses with a special spectral, temporal, and spatial resolution. There are some complex relationship may exist between the remote sensing images with different spectral, spatial, and temporal scale. In this study, we try to use a nonlinear regression model - Cubist regression tree model to mining the spectral relationship between the image bands. The Landsat5 TM image was used as reference image to collect samples to train Cubist model, and then the target image - SPOT5 image was used to predict its lacked TM-liked band1 and band7 with the TM-trained Cubist model. The experiments shows that the Cubist nonlinear regression model could simulate TM band1 and band7 with a high accuracy and the TM-trained Cubist model also could be used to predict SPOT5 lacked TM-liked band1 and band7.

In this paper, we propose a hyperspectral image segmentation algorithm which combines classification and segmentation into Conditional Random Field(CRF) framework. The classification step is implemented using Gaussian process which gives the exact class probabilities of a pixel. The classification result is treated as the single-pixel model in CRF framework, by which classification and segmentation are combined together. Through the CRF, the spatial and spectral constraints on pixel classification are exploited. As a result, experimental results on real hyperspectral image show that the segmentation precision has been much improved.

The performance of infrared focal plane array (IRFPA) is known to be affected by the presence of spatial fixed pattern noise (FPN) that is superimposed on the true image. Scene-based nonuniformity correction (NUC) algorithms are widely concerned since they only need the readout infrared data captured by the imaging system during its normal operation. A novel adaptive NUC algorithm is proposed using the sparse prior that when derivative filters are applied to infrared images, the filter outputs tends to be sparse. A change detection module based on results of derivative filters is introduced to avoid stationary object being learned into the background, so the ghosting artifact is eliminated effectively. The performance of the new algorithm is evaluated with both real and simulated imagery.

As to multi-sensor image registration, a novel algorithm for registration synthetic aperture radar (SAR) image to Optical image based on Weight Hausdorff Distance Matrix (WHDM) Metric is proposed in the paper. In the proposed method, edge feature is used for common feature, which can solve the key problem brought by different senor. WHDM is designed as a similarity measure. Experimental results using synthetic and real images demonstrate that the proposed WHDM metric outperforms most existed Hausdorff Distance based (HD-based) methods, especially in higher level noise and that the proposed algorithm is robust, and can achieve high registration accuracy.

Noise filtering is an essential part of any image processor, whether the final image is utilized for visual interpretation or for automatic analysis. Arithmetic Mean Filter (AMF) and Standard Median Filter (SM) tend to work well for fixed-valued impulses but poorly for random-valued impulse noise. The authors review the current trend of image filter development. Some papers present new ways to identify corrupted pixels, while others strongly emphasize on suppressing the noise ratio. And some summarized the both and combined to present new complicated scheme. As widely known, Vector Median Filter (VMF) has the disadvantage of replacing too many uncorrupted image pixels. New method form R. H. Chan, Chung-Wa Ho, and M. Nikolova in 2005 is capable of restoring images corrupted by salt-and-pepper noise with extremely high noise ratio, but the calculation is so complex that it is only can be treated as a post-processing image enhancement procedure. In the fact, the modern imaging equipment is good enough and will not produce more than three percent of salt-and-pepper noise. But as a pretreatment of Image Analysis, filter should focus on preservation the original details and simple or fast for the engineering. Our purpose in this paper is to present a simple scheme to preserve uncorrupted, original pixels, but still enables to remove corrupted ones with a good balance the algorithm complexity and the efficiency.

As the development of Automatic Target Recognition (ATR) technology, the performance evaluation for it becomes more and more important. Image Quality is a very important part of ATR performance evaluation. Whether the image quality is good or bad is tightly related with algorithm performance. In this paper, we construct a system of image quality assessment related to object matching task. In our framework, image quality measures published and newly proposed are analyzed first, and then a set of effective measures are selected from above. At last, we construct a model of relationship between selected measures and matching. Given an image, the system will indicate whether an image is suitable for a specific matching task. Gray level correlation matching and histograms of oriented gradients -matching are demonstrated and their experimental result shows that the image quality assessment system works effectively.

On the basis of analyzing the characteristics of the stereo image pair obtained by the remote sensing mapping, considering the continual increment of data obtained by stereo mapping satellite on orbit and the trend of the hardware development, a joint compression algorithm for the stereo image pairs based on the classified characters is proposed. First, stereo image pairs are matched through SIFT feature matching; and then the images are classified adaptively by the features according to the matching results. For the region where the characteristics are flat, the predicted residual-error image is coded based on the coarse match and the fine match using the adaptive block in order to improve the accuracy of matching with the linear prediction for the radiation compensation. For the feature-rich region, according to the correlation of the images, individual compression or joint compression is selected. When the joint compression algorithm is selected, the match alignment of the left and right image pair is done by the affine model estimation and the linear prediction is adapted to the radiation compensation. Because different compression scheme is adopted for different characteristics regions, the results show that the PSNR and the geometric precision of the reconstructed images are improved effectively, especially when the images contain many characteristics of elevation information.

In this paper, based on the theory of down-scaling, we propose the methods for linear mixed model disaggregate mixed pixels in coarse resolution images. Exploiting information about within mixed pixel each component fractional cover derived from high spatial resolution classification map, Sub-pixel reflectance for the different land-cover classes are calculated by solving a linear system of equations for each pixel of a coarse resolution image and producing the subpixel level NDVI time series curve of different component. Results showed that application of the algorithm provided good estimates of sub-pixel NDVI time series even for poorly represented land-cover classes. The main advantage of the proposed technique is that could analysis the land-use and vegetation biomass change better.

Selection of suitable matching area is one of the key issues for image-matching-aided navigation system,but it is also a very challenging mission, especially with the multi-source image matching tasks. In this paper, a novel method to analyze the matching suitability of the satellite optical photograph to the realtime SAR in candidate flying regions is put forward. At first, several typical low-level image features are extracted. Then manifold learning is used to reduce the dimension of the sampled features, so as to generate new high-level image features with better discrimination ability. Finally, with the new features generated by manifold learning, we used support vector machines (SVM) to divide the candidate regions into two classes for suitable or unsuitable for matching. The experimental result shown that the proposed method is valid and effective.

License plate location is the basis of LPR, and it is the most important part of it. Tilt vehicle license plate has an adverse effect on its character segmentation and recognition. In this paper, tilt models of a plate are analyzed and a novel approach for number plate tilt correction is presented. Horizontal incline angle is obtained by combining horizontal or vertical edge of a plate with its rotation projection. Bilinear interpolation rotation correction is performed to the plate which is tilt to the horizontal line. Experimental results show that the method can be implemented easily and offers robustness when dealing with dirty number plates and license plates in variant lighting conditions.

A new blur identification and restoration method is presented. We observe that blurring increases the second-order central moment (SOCM) of image and introduce a new parametric blur identification method by minimizing SOCM. The method applied to finite support images, in which the scene consists of a finite extent object against a uniformly black, grey or white background. The method has been validated by direct comparisons with other methods on simulated images. Our experiments show that the SOCM minimization measurements match well with methods than maximize PSNR.

This paper presents a robust pedestrian detection algorithm that works on infrared imageries. Our algorithm is applicable to images captured from surveillance infrastructure as well as moving platforms. Firstly, we introduce a local binary pattern (LBP) texture feature for infrared pedestrian representation. Secondly, motivated by the recent success of multiple cues pedestrian detection in visual imagery, we combine both shape and binary pattern texture features for effective infrared pedestrian description, providing a level of robustness to variations in pedestrian shape and appearance in infrared images. Finally, a support vector machine (SVM) classifier is utilized to classify sub-windows into pedestrians or background. Experimental results demonstrate the robustness and effectiveness of our method.

The rainfall process in autumn of Chengdu region has significant regional characteristics. Since GPS detection technology has characteristics such as all-weather, high accuracy, high spatial and temporal resolution and low cost, its tracking and monitoring technique on water vapor has achieved rapid developments in recent years. In this paper, it makes use of GPS-PWV data from 6 foundation GPS observation stations of GPS observation network in Chengdu region from September 2007 to November 2007 and from September 2008 to November 2008 these two falls which have 30min intervals. Fast Fourier transform was used to obtain the variation principle that in autumn the rainfalls change in the time period and there also has one quarter season within the fall, which is around 22 days or so. After the analysis, it finds that PWV drastically decreases at late October which is closely correlated to the local climate changing cycle. After we conduct composite analysis on diurnal cycle by integrating PWV with other meteorological elements we can find: There is a negative correlation between PWV and temperature; there is more obvious positive correlation between humidity and PWV. GPS precipitable water vapor may increase or maintain the characteristics within high value area from midnight to early morning which can bitterly correspond with the actual rainfall process of this region.

Completely different from many exiting multi-source image fusion methods, a novel color image fusion method for a single original color image, combining the multi-scale Retinex (MSR) with directional support value transform (DSVT), is presented in this paper. The applied MSR is an (centre/surround-based) Retinex algorithm and the directional support value transform, an anisotropic and multi-scale transform, is deduced under the weighted mapping least squares support vector machine (LS-SVM) framework. Using the MSR to an original color image, an enhanced color image, producing more detail information hidden in shadow areas of original color image but easily lapsing into color distortion, is obtained. In the HSV color space, the good color information of original color image and more detail information of enhanced color image are integrated into a fused color image based on the DSVT. Series of color images under different environment are chosen for color image fusion experiments and the performance of DSVT is compared with other methods also used in color image fusion, including Laplacian pyramid, discrete wavelet transform and support value transform. The experimental results demonstrate that: the proposed color image fusion approach is effectively making the fused color images not only present more clearly detail information but also maintain color fidelity; DSVT is superior to other three methods used in color image fusion.

With the development of sensors, the spatial and spectral resolutions of remote sensing data are getting much higher, which presents new possibilities and challenges for pixel based material classification. When most of the methods available in literature extract features in spectrum domain for land material classification, the rich information contained in hyperspectral data is not fully used. As a result, the classification accuracies reported in literature are not satisfying. In this work, we aim to use joint spatial and spectral analysis technique to extract information about signal variances in space, spectrum and joint space-spectrum domain. The feature thus extracted can better represent the signal variances and can thus improve overall classification accuracy.

The classification of locust habitats is one of the most important tasks in terms of the monitoring and controlling of the locusts damages. Taking the Bohai development zone in Hebei province as the study area, this paper generated firstly the 14 scenes of temporal NDVI data from January 31 to December 9, 2010 based on image preprocessing and NDVI calculation from HJ-1 satellites. And then the classification system was developed according to the characteristics of the study area and locust habitats, including dense reeds, sparse reeds, folder wasteland, grassland, crop1, crops2, alfalfa, orchard, soil, salt water, building and road. At last, this paper classified the locust habitats using decision tree method based on the multi-temporal NDVI data. Verification data collected through ground surveys and 14 scenes of temporal CCD data were used for assessing the accuracy of the classified results. The results showed that some surface features prone to misclassification in a single phase for they have similar spectral characteristics were easy to classify from multitemporal data because their different temporal signature.

It is an important work to measure insulator geometric parameters for preventing pollution flashover on power transmission lines. This paper presents an effective method to measure insulator parameters based on non-touch photogrammetric method. In this paper, the insulator reconstruction takes full advantage of the rotating body's geometry structure about the symmetry axis. The spindle of insulator can be calculated from its profile extracted from multiple insulator images acquired by calibrated cameras and radius is obtained by the homologous image points of insulator profiles. Results from real data are presented, demonstrating the efficiency of the proposed methods.

A novel speckle patterns processing method is presented using multi-scale wavelet techniques. Laser speckle patterns generated from the sample contained abundant information. In this paper, we propose a method using wavelet entropy techniques to analyze the speckle patterns and exact the information on the sample surface. In our case, we used this approach to test the solar silicon cell surface profiles based on the sym8 orthogonal wavelet family. According different wavelet entropy values, the micro-structure of different solar silicon cell surfaces were comparative analyzed. Furthermore, we studied the AFM and reflective spectra of the wafer. Results show that the wavelet entropy speckle processing method is effective and accurate. And the experiment proved that this method is a useful tool to investigate the surface profile quality.

This paper deals with deconvolution problem for passive millimeter wave images with poor resolution and low SNR. A passive millimeter wave images super-resolution algorithm based on semi-blind deconvolution is put forward. The proposed method is based on two characteristic of imaging system. First, the PSF of imaging system is certain, can be modeled by parametric function. Second, the noise imposes different influence degree on the low frequency and high frequency parts of the pass-band of the image, the low frequency and high frequency part have high and low SNR, respectively.The image is decomposed using the bilateral filtering into low frequency base layer and high frequency detail layer. The base layer contains the large-scale structures and nearly frees with noise thus has the higher SNR, whereas the detail layer includes both small-scale details and noise and has the lower SNR. The base layer is restored by semi-blind deconvolution. The system PSF is modeled as a parametric Gaussian form. Edge structures information of the image is extracted basing on Mumford-Shah model and used to adjust the regularization term adaptively, and iterative method is used to estimate image and blurred kernel parameters. The detail layer is adaptively denoised by combining the joint bilateral filtering method and with edge preservation in the guidance of the base layer. Finally, the high resolution image is obtained by combining the base and detail layers. Comparative experimental results show that the proposed method can effectively suppress noise, reduce artifacts, and improve the spatial resolution.

This paper deals with edge-preserving interpolation for passive millimeter wave images with poor resolution and low SNR. A hybrid interpolation method basing on image decomposition via bilateral filtering is proposed. The low resolution and noisy image is first decomposed using the bilateral filter into the base and detail layers which represent large and small scale features, respectively. The base layer contains the large-scale structures and nearly frees with noise thus has the higher SNR, whereas the detail layer includes both small-scale details and noise and has the lower SNR. The detail layer is adaptively denoised by the joint bilateral filtering method and subsequently interpolated with edge preservation in the guidance of the base layer. The base layer is interpolated with edge-preserving method directly. Finally, the high resolution image is obtained by combining the base and detail layers. Experimental results show that the proposed method outperforms the conventional methods while preserving edges and suppressing jagging thus is suited for PMMW images interpolation to enhance resolution.

In the application of the video contactless measurement, the quality of the image taken from underwater is not very well. It is well known that automatic image segmental method cannot provide acceptable segmentation result with low quality single still image. Snake algorithm can provide better result in this case with the aiding of human. However, sometimes the segmental result of Snake may far from the initial segmental contour drawn by user. Livewire algorithm can keep the location of the seed points that user selected nailed from the beginning to the end. But the contour may have burrs when the image's noise is quite high and the contrast is low. In this paper, we modified the cost function of Livewire algorithm and proposed a new segmentation method that can be used for single still image segmentation with high noise and low contrast.

The Principle Neighborhood Dictionary (PND) filter projects the image patches onto a lower dimensional subspace using Principle Component analysis (PCA), based on which the similarity measure of image patch can be computed with a higher accuracy for the nonlocal means (NLM) algorithm. In this paper, a new PND filter for synthetic aperture radar (SAR) image despeckling is presented, in which a new distance that adapts to the multiplicative speckle noise is derived. Compared with the commonly used Euclidean distance in NLM, the new distance measure improves the accuracy of the similarity measure of speckled patches in SAR images. The proposed method is validated on simulated and real SAR images through comparisons with other classical despeckling methods.

Collusion tolerance is an essential requirement for digital fingerprinting, and existing collusion-resistant encoding and tracking methods are generally based on combinatorics. However, those methods have some deficiencies such as low tracking efficiency and large fingerprint database for matching. Owing to those weaknesses, a hybrid fingerprint encoding method based on residual characters is proposed in this paper. The fingerprint is generated by encoding the user information into a binary image composed of letter, numeral and Chinese character. As a result of the complex structure of Chinese character, more residue characters can be reserved when the fingerprint is damaged. Thus, the fingerprint has a good performance in anti-tampering. Meanwhile, it has the capacity for large users because of the diversity of Chinese character. In addition, the paper proposes a tracking method based on grid feature, which greatly reduces the computational complexity and improves the tracking efficiency. The robustness and collusion tolerance of the digital fingerprinting scheme are confirmed by theoretical analysis and experimental results.

The multiplicative noise model is often used to describe the speckle noise in the SAR image. This speckling process defines the conditional probability of the SAR image intensity I from RCS ó. The MMSE algorithm of the SAR image reconstruction is basis on the multiplicative noise model. However, the MMSE algorithm does not define how calculating the look number of the SAR image. This paper advises an algorithm calculating the look number of the SAR image. This algorithm is basis on the local statistical information. Therefore, the MMSE algorithm can adapt all kinds of the SAR images. Furthermore, when the iteration MMSE is used, the MMSE algorithm can adapt the change of SAR image statistical information.

Contourlet-domain hidden markov tree(HMT) model can reflect the coefficients' dependencies of different scales and directions, then a SAR and optical image fusion algorithm based on it is proposed. Firstly source images are decomposed by Contourlet transform, low frequency and high frequency subband coefficients are obtained; secondly, the high frequency subband coefficients are modeled using hidden markov tree and the model is trained using Expectation-Maximization(EM) algorithm to get the posterior probability of the coefficients; Thirdly, using the posterior probability to guide the fusion rules' design of high frequency subband coefficients in order to preserve the salient features of original images well and obtain good noise suppression; Finally, the fusion coefficients are inversed to get the final fusion result. SAR and Panchromatic images were taken to fuse, and the results were evaluated by Difference Coefficient, Correlation Coefficient and Signal-Noise Ration. The experimental result shows that the proposed algorithm is better than traditional fusion algorithms based on Contourlet and wavelet-domain hidden markov tree model.

A novel infrared image denosing algorithm is proposed based on adaptive dictionary learning over sparse and redundant representations. The dictionary which can yield sparse representations is learned from the corrupted infrared image itself, instead of using the prechosen set of basis functions such as curvelet or contourlet. Meanwhile, the over-completed dictionary is updated adaptively in the online learning procedure other than batch learning method to improve the learning performance. And the learning and denoising procedure are fused together into one iterated process naturally and properly. Experimental results demonstrate the effectiveness of the denosing algorithm for infrared images.

An improved NSCT based shrinking threshold denoising algorithm for infrared image is proposed. The improved NSCT is constructed based on the Nonseparable Wavelet Transform via non-linear lifting method with redundancy structure, which can produce better image processing performance for its better detail capturing capability, shift invariance, multiresolution and multi-direction. After analyzing the current threshold functions and threshold selecting methods, an novel threshold function suitable for the improved NSCT is established with high-level continuous derivative to improve the denosing performance for infrared image. Experimental results show that the proposed algorithm has better denoising performance and detail-preserving capability.

Image restoration is one of the most classical problems in image processing. The main issue of image restoration is deblurring as well as preserving the fine details. In order to restore the high quality image, we propose a compound regularization method which combines the tetrolet-based sparsity and a new weighted adaptive total variation (ATV). Tetrolet transform is a geometric adaptive Haar-type wavelet transform. It finds the optimal partition to fit the local image structures and the tetrolet coefficients can capture the textures and details information in different image scales. ATV adds two directional gradient operators into the original anisotropic TV. It not only seeks the intensity continuity horizontally and vertically, but also seeks the intensity continuity diagonally. Combining the tetrolet-based sparsity and ATV together, our model can restore the local structures and details by the tetrolet-based sparsity regularization while suppress the noise and recover piecewise smooth images with sharp edges along four directions by the ATV regularization. For solving the minimizing problem, we propose an algorithm which consists of the variable splitting method and the Dual Douglas-Rachford splitting method. The Experimental results demonstrate the efficiency of our image restoration method for preserving the structure details and the sharp edges of image.