This PDF file contains the front matter associated with SPIE Proceedings Volume 6746, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Fractal geometry provides the correct scientific approach to model natural environments. The altimeter of the Cassini mission is acquiring profiles of the Titan surfaces. In this paper the rationale for a fractal analysis of the profiles acquired by the Cassini altimeter is presented. The quantitative analysis proves that the fractal models provide meaningful information on the Titan surface. It is also shown that the classical (non-fractal) analysis leads to erroneous results.

TerraSAR-X is Germany's new radar remote sensing flagship. It carries an advanced high-resolution X-band SAR instrument. The key element of the system is the active phased array antenna nominally operated with a bandwidth of 100 MHz or 150 MHz and an experimental 300 MHz capability. The instrument's flexibility with respect to electronic beam steering and pulse-to-pulse polarization switching allows the acquisition of SAR data in Stripmap, Spotlight and ScanSAR imaging configurations in different polarization modes for a wide range of incidence angles. The mission is implemented in the framework of a public-private partnership between the German Aerospace Center (DLR) and EADS Astrium GmbH Germany and will provide high resolution SAR data products for commercial use and scientific exploitation. Processing of the payload data will be performed at DLR's Payload Ground Segment (PGS) for TerraSAR-X. The central part of PGS is the TerraSAR Multi-Mode SAR Processor (TMSP) focusing the SAR data in a unified way for the different imaging configurations. A wide range of processing options spanning from phase preserving complex products in slant range geometry to orthorectified terrain corrected intensity images lead to a comprehensive collection of SAR product types and variants. During the 5 months lasting commissioning phase the complete processing chain will be properly tuned and adjusted. The TMSP algorithms have to be configured, e.g. thresholds for calibration pulse analysis, estimation window sizes for SAR data analysis, parameterization of estimation algorithms. Also the configuration of product variants with respect to resolution and radiometric quality will be checked and refined. This paper shortly reviews the different imaging configurations and product variants and gives a report on the SAR processor checkout activities and presents the first results.

High Resolution (HR) Synthetic Aperture Radar (SAR) Single Look Complex (SLC) observations, mainly of strong scattering scenes or objects show phase patterns. Phase patterns may occur due to the system behavior or they may be signatures of the imaged objects. Since state of the art stochastic models of SAR SLC data describe mainly the pixel information. Now studies are needed to elaborate better models for the full information content. Thus, new statistical models of HR SAR SLC are proposed, they aim at the characterization of the spatial phase feature of Polarimetric SAR (PolSAR) SLC data, i.e. they describe multi-band, complex valued textures. The definition of texture must be changed because it is not anymore characterizing the optical features but the electromagnetic properties of the illuminated targets. The content of the SAR image is a stochastic process characterized from its own structure and geometry, which differs from the real one of the illuminated scene, and is dominated from strong scatterers. Nevertheless we are going to accept the classical texture definition, inherited from computer vision, in homogeneous areas and, furthermore, we are going to extend it for a characterization of isolated and structured objects The proposed models are in the class of simultaneous Auto-Regressive (sAR) defined on a generalized set of cliques in the pixel vicinity. Models may have different orders, thus capturing different degrees of the data complexity. To cope with the problem of estimation and model order selection Bayesian inference is used. The results are presented on PolSAR data.

With the increase of the Synthetic Aperture Radar (SAR) sensor resolution, a more detailed analysis and a finer description of SAR images are needed. Nevertheless, when dealing with urban areas, the high diversity of manmade structures combined with the complexity of the scattering processes makes the analysis and information extraction, from high resolution SAR images over such areas, not easily reachable. In general, an automatic full understanding of the scene requires the capability to identify both relevant and reliable signatures (called also features), depending on variable image acquisition geometry, arbitrary objects poses and configurations. Then, since SAR images are formed, by coherently adding the scattered radiations from the components of the illuminated scene objects, we can make the assumption that, the SAR image is a superposition of different sources. Following this approach, one alternative for a better understanding of the HR SAR scenes, could be a combination between the Principal Components Analysis (PCA) and the Independent Components Analysis (ICA) decompositions. Indeed, while the PCA exploits at most the information stored in the sample covariance matrix, the ICA is a de-mixing process whose goal is to express a set of random variables as linear combinations of statistically independent component variables. Such an approach could be useful for the recognition of urban structures, in HR SAR images. In this paper, we compare the Principal Components (PCs) to the Independent Components (ICs). Furthermore, we present some preliminary results on learning and decomposing SAR images, using PCA and ICA.

Multitemporal analysis of Synthetic Aperture Radar (SAR) images has gained an ever increasing attention due to the availability of several satellite platforms with different revisit times and to the intrinsic capability of the SAR system of producing all-weather observations. As a drawback, automated analysis in general and change detection in particular are made difficult by the inherent noisiness of SAR imagery. Even if a preprocessing step aimed at speckle reduction is adopted, most of algorithms borrowed from computer vision cannot be profitably used. In this work, a novel pixel feature suitable for change analysis is derived from information-theoretic concepts. It does not require preliminary despeckling and capable of providing accurate change maps from a couple of SAR images. The rationale is that the negative of logarithm of the probability of an amplitude level in one image conditional to the level of the same pixel in the other image conveys an information on the amount of change occurred between the two passes. Experimental results carried out on two couples of multitemporal SAR images demonstrate that the proposed IT feature outperforms the Log-Ratio in terms of capability of discriminating flooded areas and outlining their borders.

During the first two years of the Cassini mission, a great amount of data dealing with Titan's surface has been collected. The analysis derived from the SAR imagery reflects the complex Titan's surface morphology with peculiar features such as: dark and bright areas (Ta, T3), periodic structure ("sand dunes") and, above all, lake-like features, firstly observed during the T16 flyby on 22 July 2006 and good candidates to be filled with liquid hydrocarbons. In this paper the modeling description of lakes is addressed by means of a double layer model. Subsequently this model is introduced into a Bayesian framework for the purpose of inferring the likely ranges of some lake parameter and in particular of the optical thickness of the hypothesized liquid hydrocarbons layer. The main idea is to use the information contained in the parameter probability density function, which describes how probability is distributed among the different values of parameters according to the various scenarios considered. The analysis has been carried out on lakes and surrounding areas detected on flybys T16, T19, T25 and has given plausible hypothesis on the lake composition and optical depth. Furthermore a first attempt has been made to exploit information from radiometric data. The typical inverse relationship between radar and radiometric data has been verified on some regions of interest chosen on the T25 flyby. This investigation may be used in a context of radar and radiometric data fusion to extract information on the optical thickness of lakes and other surface features.

Interferometric Synthetic Aperture Radar has the capability to provide the user with the 3-D-Information of land surfaces. To gather data with high height estimation accuracy it is necessary to use a wide interferometric baseline or a high radar frequency. However the problem of resolving the phase ambiguity at smaller wavelengths is more critical than at longer wavelengths, as the unambiguous height interval is inversely proportional to the radar wavelength. To solve this shortcoming, a multiple baseline approach can be used with a number of neighbouring horns and an increasing baselength going from narrow to wide. The narrowest, corresponding to adjacent horns, is then assumed to be unambiguous in phase. This initial interferogram is used as a starting point for the algorithm, which in the next step, unwraps the interferogram with the next wider baseline using the coarse height information to solve the phase ambiguities. This process is repeated consecutively until the interferogram with highest precision is unwrapped. On the expense of this multi-channel-approach the algorithm is simple and robust, and even the amount of processing time is reduced considerably, compared to traditional methods. The multiple baseline approach is especially adequate for millimeterwave radars as antenna horns with relatively small aperture can be used, while a sufficient 3-dB beamwidth is maintained. The paper describes the multiple baseline algorithm and shows the results of tests on real data and a synthetic area. Possibilities and limitations of this approach are discussed. Examples of digital elevation maps derived from measured data at millimeterwaves are shown.

In this paper we have deduced the stability and availability of clutter position estimation, the average value estimation and middle value estimation, which can construct the choice of estimation method, and we proposed a way of target detection base on the corrected clutter position estimation. In this way, it decayed the influence of clutter position estimation in single look map or in multi-look map.

Along Track Interferometric Synthetic Aperture Radar (AT-InSAR) systems use more than one SAR antennas (typically two), mounted on the same platform and displaced along the platform moving direction, to detect slow ground moving targets. The phase of the ATI signal is related to the target motion parameters and may thus be used to estimate the radial velocity. In this paper we approach the velocity estimation problem using statistical techniques based on the statistical distribution of the measured interferometric phases. We analyze the radial velocity estimation with respect to ATI system parameters, such as velocity values, the signal to clutter ratio (SCR), the clutter to noise ratio (CNR), considering a deterministic target whose velocity is estimated using a Gaussian model. This model allows to take into account the lack of knowledge of the target radar cross section (RCS) values and provides an analytical form for the interferometric phase probability density function. Simulations results show that the adoption of Maximum Likelihood (ML) techniques, to perform a joint estimation of velocity and SCR, and multi-channel configurations, to overcome ambiguities problems, provide very good velocity estimation accuracy.

The main aim of this paper is the investigation of the potential of different radar frequencies for surface parameters estimation. Two case studies have been analyzed. The first is devoted to obtain soil moisture and roughness of bare soils from a combination of X, C and L band SAR images acquired during the SIRC/XSAR mission. The results indicate that the combination of the three bands is particularly useful when roughness parameter estimation is performed. Also the use of the cross-polarization VH has been exploited. The second case study is focused on vegetation water content estimates by using a combination of C and L band data acquired during the SMEX'02 experiment on densely vegetated fields. The errors on estimates are reduced when both C and L band data and a correction for roughness are used.

One of the major projects of the Institute of Geology & Mineral Exploration (IGME) is called "Urban Geology". In the frame of that project there is need for a high accuracy DEM covering the whole country. The DEM should be used for the orthorectification of high resolution images and other applications such as slope map creation, environmental planning et.c. ASTER and SRTM are two possible sources for DEM covering the whole country. According to the specifications the ASTER vertical accuracy of DEM is about 20m with 95% confidence while the horizontal geolocation accuracy appears to be better than 50 m. More recent studies have shown that the use of GCP's resulted in a plannimetric accuracy of 15 m and in a near pixel size vertical accuracy. The Shuttle Radar Topography Mission (SRTM), used an Interferometric Synthetic Aperture Radar (IFSAR) instrument to produce a near-global digital elevation map of the earth's land surface with 16 m absolute vertical height accuracy at 30 meter postings. An SRTM 3-arc-second product (90m resolution) is available for the entire world. In this paper we examine the accuracy of SRTM and ASTER DEMs in comparison to the accuracy of the 1/5.000 topographic maps. The area of study is the broader area of Sparti, Greece. After a first control for random or systematic errors a statistical analysis was done. A DEM derived from digitized contours of the 1:5.000 topographic maps was created and compared with ASTER and SRTM derived DEMs. Fifty-five points of known elevation have been used to estimate the accuracy of these three DEMs. Slope and aspect maps were created and compared. The elevation difference between the three DEMs was calculated. 2D RMSE, correlation and the percentile value were also computed. The three DEMs were used for the orthorectification of very high resolution data and the final orthophotos were compared.

The properties of single look complex SAR satellite images have already been analyzed by many investigators. A common belief is that, apart from inverse SAR methods or polarimetric applications, no information can be gained from the phase of each pixel. This belief is based on the assumption that we obtain uniformly distributed random phases when a sufficient number of small-scale scatterers are mixed in each image pixel. However, the random phase assumption does no longer hold for typical high resolution urban remote sensing scenes, when a limited number of prominent human-made scatterers with near-regular shape and sub-meter size lead to correlated phase patterns. If the pixel size shrinks to a critical threshold of about 1 meter, the reflectance of built-up urban scenes becomes dominated by typical metal reflectors, corner-like structures, and multiple scattering. The resulting phases are hard to model, but one can try to classify a scene based on the phase characteristics of neighboring image pixels. We provide a "cooking recipe" of how to analyze existing phase patterns that extend over neighboring pixels.

Conventional ground segments for spaceborne SAR instruments collect raw SAR data and convert them into standard image and interferometry products. Typical examples are the routine generation of complex, detected, or geocoded image products. These data are archived and accessible via user interfaces linked to browsing tools that use catalogues with quicklooks, metadata, etc. On the other hand, during the last years, considerable effort has been spent in the design of data mining systems and specific image information mining techniques that allow the retrieval of images from large archives based on their content. We will describe how the PIMS ground segment architecture combines product generation, archiving, and cataloguing with image information mining functions permitting automated feature extraction as well as interactive data analyses by users of various disciplines.

Both real-time rate and resolution both are key indexes of Synthetic Aperture Radar(SAR)imaging, but there is a conflict between them. Real-time imaging becomes difficult because of the large computational requirement posed by high-resolution processing. Parallel computing is an effective approach for real-time processing. In previous research, coarse and medium grained parallel algorithms for SAR imaging have been presented. Although they can significantly improve the processing speed, the quality of image has been ignored. Subaperture is widely used in high-resolution SAR. Compared with full aperture processing, it can compensate the motion errors more accurately and get better images. Whereas, subaperture processing can't be applied in existing parallel imaging algorithms because of they are all based on full aperture processing, which restricts the application of existing algorithms in high-resolution SAR parallel imaging. This paper presents a parallel imaging algorithm for high-resolution SAR, through which we can obtain high-resolution SAR image while achieving good computation efficiency. It combines chirp-scaling algorithm with subaperture processing. The new algorithm can highly effectively run on parallel computer, in which each node has the same load. It reduces the large communication requirement posed by three transposes through designing CS processing for subaperture data, and it has better parallel scalability, which means that it can be used on larger parallel computer without deducing the image quality. The experiments on SGI Origin2000 have proved that, compared with medium grained parallel CS algorithm, the algorithm presented in this paper is more suitable for high-resolution SAR parallel imaging.

This paper introduces a simple method to obtain a much higher resolution than the designed resolution in both azimuthal and rang directions for SAR by synthesizing the data taken by repeat passes without increasing any complexity in SAR hardware and satellite platform. The basic idea of the method is to firstly establish the equivalence between the signal models of repeat pass SAR signals in both azithumal and range directions and the signal model of stepped frequency chirp signals (SFCSs) when some conditions are presumed, i.e. for range direction, interferometric condition is required and for azimuthal direction, a small squint angle increase between repeat passes is required, and then using the already proposed method for SFCSs compression to process the data of repeat passes. In the course of processing, each observation in range direction or in azimuthal direction is treated as a subchirp in SFCSs. The major facts affecting the final resolution one could get are investigated and found. They are the relative range measurement accuracy and the absolute squint angle measurement accuracy between repeat passes. Detailed derivations and simulations are presented to show the effectiveness of the method.