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The Lee sigma filter was developed in 1983 based on the simple concept of two-sigma probability, and it was reasonably effective in speckle filtering. However, deficiencies were discovered in producing biased estimation and in blurring and depressing strong reflected targets. The advancement of synthetic aperture radar (SAR) technology with high-resolution data of large dimensions demands better and efficient speckle filtering algorithms. In this paper, we extend and improve the Lee sigma filter by eliminating these deficiencies. The bias problem is solved by redefining the sigma range based on the speckle probability density functions. To mitigate the problems of blurring and depressing strong reflective scatterers, a target signature preservation technique is developed. In addition, we incorporate the minimum-mean-square-error estimator for adaptive speckle reduction. Simulated SAR data are used to quantitatively evaluate the characteristics of this improved sigma filter and to validate its effectiveness. The proposed algorithm is applied to spaceborne and airborne SAR data to demonstrate its overall speckle filtering characteristics as compared with other algorithms. This improved sigma filter remains simple in concept and is computationally efficient but without the deficiencies of the original Lee sigma filter. Index Terms---Sigma filter, speckle, speckle filtering, synthetic aperture radar (SAR).

Compact polarimetry is a technique that allows construction of pseudo quad-pol information from dual-polarization synthetic aperture radar (SAR) systems. Compact polarimetry showed promise of being able to reduce the complexity, cost, mass, and data rate of a SAR system while attempting to maintain many capabilities of a fully polarimetric system. In this paper, we study different transmit/receive configurations to determine which polarimetric configurations allow for superior reconstruction of the fully polarimetric data. We discuss modifications of the original reconstruction algorithm proposed by Souyris et al., which show potential to better reconstruct fully polarimetric data. Index Terms--Polarimetric radar, radar polarimetry, radar signature.

Entropy, alpha, and anisotropy (H/[alpha]/A) of the polarimetric target decomposition have been an effective and popular tool for polarimetric synthetic aperture radar (SAR) image analysis and for a geophysical parameter estimation. However, multilook processing can severely affect the values of these parameters. In this paper, a Monte Carlo simulation is used to evaluate and remove the bias generated by the multilook effect on these parameters for various media composed of grassland, forest, and urban returns. Due to insufficient averaging, entropy is underestimated, and anisotropy is overestimated. We also found that the bias in the alpha angle can be either underestimated or overestimated depending on scattering mechanisms. Based on simulation results, efficient bias removal procedures have been developed. In particular, the entropy bias can be precisely corrected, and the amount of correction is independent of the radar frequency and SAR systems. Data from L-band Advanced Land Observing Satellite/phased array type L-band SAR, German Aerospace Research Center (DLR)/enhanced SAR, Jet Propulsion Laboratory (JPL)/airborne SAR, and X-band polarimetric and interferometric SAR are used for demonstration in this paper. Index Terms--Monte Carlo simulation, multilooking, polarimetric synthetic aperture radar (PolSAR), polarimetric target decomposition.

In this paper, a new method to filter coherency matrices of polarimetric or interferometric data is presented. For each pixel, an adaptive neighborhood (AN) is determined by a region growing technique driven exclusively by the intensity image information. All the available intensity images of the polarimetric and interferometric terms are fused in the region growing process to ensure the validity of the stationarity assumption. Afterward, all the pixels within the obtained AN are used to yield the filtered values of the polarimetric and interferometric coherency matrices, which can be derived either by direct complex muitilooking or from the locally linear minimum mean-squared error (LLMMSE) estimator. The entropy/alpha/anisotropy decomposition is then applied to the estimated polarimetric coherency matrices, and coherence optimization is performed on the estimated polarimetric and interferometric coherency matrices. Using this decomposition, unsupervised classification for land applications by an iterative algorithm based on a complex Wishart density function is also applied. The method has been tested on airborne high-resolution polarimetric interferometric synthetic aperture radar (POL-InSAR) images (Oberpfaffenhofen area--German Space Agency). For comparison purposes, the two estimation techniques (complex multilooking and LLMMSE) were tested using three different spatial supports: a fix-sized symmetric neighborhood (boxcar filter), directional nonsymmetric windows, and the proposed AN. Subjective and objective performance analysis, including coherence edge detection, receiver operating characteristics plots, and bias reduction tables, recommends the proposed algorithm as an effective POL-InSAR postprocessing technique. Index Terms--Coherency estimation, interferometry, multivariate region growing, polarimetric synthetic aperture radar.

Fully polarimetrie synthetic aperture radar (SAR) data analysis has found wide application for terrain classification, land-use, soil moisture, and ground cover classification. Critical to all analyses and applications is accurate calibration of the relative amplitudes of and phases between the various polarimetric channels. Here we propose an a posteriori method imposing only the weakest of constraints, scattering reciprocity, on the polarimetric data. Calibration parameters are self-consistently estimated from full 4 x 4 polarimetric covariance matrices. Whilst the complete set of calibration parameters is underdetermined, we give several reasonable heuristic methods to provide a complete calibration. Stronger constraints reduce the number of independent parameters and provide an overdetermined set of equations but at a cost--the loss of polarimetric fidelity when the underlying assumptions are violated. Without recourse to in situ calibration targets, the extent of the polarimetric distortion that results from polarimetric calibration remains unknown. We apply our new method to simulated data, anechoic chamber data and polarimetric SAR imagery. We also present comparisons with alternate calibration methods and different approximate solutions of the new technique. Index Terms--Calibration, radar polarimetry.

A new concept in polarimetric synthetic aperture radar (POLSAR) speckle filtering that preserves the dominant scattering mechanism of each pixel is proposed in this paper. The basic principle is to select pixels of the same scattering characteristics to be included in the filtering process. To achieve this, the algorithm first applies the Freeman and Durden decomposition to separate pixels into three dominant scattering categories: surface, double bounce, and volume, and then unsupervised classification is applied. Speckle filtering is performed using the classification map as a mask. A single-look or multilook pixel centered in a 9 x 9 window is filtered by including only pixels in the same and two neighboring classes from the same scattering category. This filter is effective in speckle reduction, while perfectly preserving strong point target signatures, and retains edges, linear, and curved features in thePOLSAR data. The effect of speckle filtering on scattering characteristics, such as entropy, anisotropy, and alpha angle, will be discussed. Index Terms--Polarimetric synthetic aperture radar (POLSAR), radar polarimetry, speckle filtering.

In this study, independent classifications of Landsat Thematic Mapper imagery and Jet Propulsion Laboratory AirSAR were combined to create an integrated classification of pasture and other vegetation types for a study area in the agricultural zone of Western Australia. The resulting classification combines greenness and brightness information from optical data with structure and water content information from synthetic aperture radar (SAR). Field observations of vegetation type, botanical composition, ground cover percentage, wet and dry biomass, canopy height, and soil water content were collected at 34 sites representing a range of pastures, browse shrubs, and crops. An unsupervised version of the Complex Wishart classification procedure, based on preserving scattering characteristics from the Freeman and Durden backscatter decomposition, was applied to the C-, L-, and P-band polarimetric SAR data. The optical classification was carried out using a principle component analysis on the green, red, and near-infrared bands and clustering on the basis of a class centroid distance measure and knowledge of ground targets. These two classification results were then fused together. Assessment of a confusion matrix using the individual sites showed that identification of more uniform, dense, and structurally distinct canopies was better than that of more diverse, sparse, and structurally ambiguous canopies, as the former were better represented by the canopy height attribute used in the SAR classification component. The optical classification enabled correction of SAR misclassification of vegetation due to surface roughness and soil moisture effects, or similar backscatter responses from herbaceous or arboreal canopies. The results show that simplification of vegetation into groups based upon properties with sensitive responses in both the optical and SAR domains, and combination of separate SAR and optical classifications, has potential for improving classification of diverse and heterogeneous herbaceous and browse cover in grazing lands. However, collection of ground calibration data must be at an appropriate spatial scale and include canopy and surface measurements directly related to backscatter mechanisms and spectral sensitivity. Index Terms--Botanical composition, grazing lands, herbage height, land-use classification, radar polarimetry, synthetic aperture radar (SAR).

In this paper, we assess the performance of synthetic aperture radar (SAR) compact polarimetry architectures based on mixed basis measurements, where the transmitter polarization is either circular or orientated at 45[degrees]([pi]/4), and the receivers are at horizontal and vertical polarizations with respect to the radar line of sight. An original algorithm is proposed to reconstruct the full polarimetric (FP) information from this architecture. The performance assessment is twofold: it first concerns the level of information preserved in comparison with FP, both for point target analysis and crop fields classification, using L-band SIRC/XSAR images acquired over Landes forest and Jet Propulsion Laboratory AIRSAR images acquired over Flevoland. Then, it addresses the space implementation complexity, in terms of processed swath, downloading features, power budget, calibration, and ionospheric effects. The polarization uniqueness in transmission of this mixed basis mode, hereafter referred to as the [pi]/4 mode, maintains the standard lower pulse repetition frequency operation and hence maximizes the coverage of the sensor. Because of the mismatch between transmitter and receiver basis, the power budget is deteriorated by a factor of 3 dB, but it can partly be compensated. Index Terms--Crop classification, partial polarimetry, radar polarimetry, spaceborne synthetic aperture radar, symmetry properties of geophysical media, synthetic aperture radar (SAR).

Five clustering techniques are compared by classifying a polarimetric synthetic aperture radar image. The pixels are complex covariance matrices, which are known to have the complex Wishart distribution. Two techniques are fuzzy clustering algorithms based on the standard [l.sub.1] and [l.sub.2] metrics. Two others are new, combining a robust fuzzy C-means clustering technique with a distance measure based on the Wishart distribution. The fifth clustering technique is an application of the expectation-maximization algorithm assuming the data are Wishart. The clustering algorithms that are based on the Wishart are demonstrably more effective than the clustering algorithms that appeal only to the [l.sub.p] norms. The results support the conclusion that the pixel model is more important than the clustering mechanism. Index Terms--Fuzzy clustering, image classification, polarimetric synthetic aperture radar (POLSAR), synthetic aperture radar (SAR).

In this paper, we proposed an unsupervised terrain and land-use classification algorithm using polarimetric synthetic aperture radar data. Unlike other algorithms that classify pixels statistically and ignore their scattering characteristics, this algorithm not only uses a statistical classifier, but also preserves the purity of dominant polarimetric scattering properties. This algorithm uses a combination of a scattering model-based decomposition developed by Freeman and Durden and the maximum-likelihood classifier based on the complex Wishart distribution. The first step is to apply the Freeman and Durden decomposition to divide pixels into three scattering categories: surface scattering, volume scattering, and double-bounce scattering. To preserve the purity of scattering characteristics, pixels in a scattering category are restricted to be classified with other pixels in the same scattering category. An efficient and effective class initialization scheme is also devised to initially merge clusters from many small clusters in each scattering category by applying a merge criterion developed based on the Wishart distance measure. Then, the iterative Wishart classifier is applied. The stability in convergence is much superior to that of the previous algorithm using the entropy/anisotropy/Wishart classifier. Finally, an automated color rendering scheme is proposed, based on the classes' scattering category to code the pixels to resemble their natural color. This algorithm is also flexible and computationally efficient. The effectiveness of this algorithm is demonstrated using the Jet Propulsion Laboratory's AIRSAR and the German Aerospace Center's (DLR) E-SAR L-band polarimetric synthetic aperture radar images. Index Terms--Radar polarimetry, synthetic aperture radar (SAR), terrain classification.

Recently, polarimetric synthetic aperture radar (SAR) interferometry has generated much interest for forest applications. Forest heights and ground topography can be extracted based on interferometric coherence using a random volume over ground coherent mixture model. The coherence estimation is of paramount importance for the accuracy of forest height estimation. The coherence (or correlation coefficient) is a statistical average of neighboring pixels of similar scattering characteristics. The commonly used algorithm is the boxcar filter, which has the deficiency of indiscriminate averaging of neighboring pixels. The result is that coherence values are lower than they should be. In this paper, we propose a new algorithm to improve the accuracy in the coherence estimation based on speckle filtering of the 6 x 6 polarimetric interferometry matrix. Simulated images are used to verify the effectiveness of this adaptive algorithm. German Aerospace Center (DLR) L-Band E-SAR data are applied to demonstrate the improved accuracy in coherence and in forest height estimation. Index Terms--Coherence estimation, forest parameter inversion, polarimetric interferometry, synthetic aperture radar (SAR) speckle filtering.

This paper presents a method, based on a fuzzy neural network, that uses fully polarimetric information for terrain and land-use classification of synthetic aperture radar (SAR) image. The proposed approach makes use of statistical properties of polarimetric data, and takes advantage of a fuzzy neural network. A distance measure, based on a complex Wishart distribution, is applied using the fuzzy c-means clustering algorithm, and the clustering result is then incorporated into the neural network. Instead of preselecting the polarization channels to form a feature vector, all elements of the polarimetric covariance matrix serve as the target feature vector as inputs to the neural network. It is thus expected that the neural network will include fully polarimetric backscattering information for image classification. With the generalization, adaptation, and other capabilities of the neural network, information contained in the covariance matrix, such as the amplitude, the phase difference, the degree of polarization, etc., can be fully explored. A test image, acquired by the .let Propulsion Laboratory Airborne SAR (AIRSAR) system, is used to demonstrate the advantages of the proposed method. It is shown that the proposed approach can greatly enhance the adaptability and the flexibility giving fully polarimetric SAR for terrain cover classification. The integration of fuzzy c-means (FCM) and fast generalization dynamic learning neural network (DLNN) capabilities makes the proposed algorithm an attractive and alternative method for polarimetric SAR classification. Index Terms--Fuzzy c-means, neural classification, polarimetric synthetic aperture radar, speckle filtering.

We investigate in this paper the one-point statistical properties of the backscattered power derived by polarization synthesis of polarimetric synthetic aperture radar (SAR) observations. In particular, we focus our attention on the normalized second moment of intensity and its dependency on the polarization state. For the analysis of this dependency, a novel graphical representation--an extension of the polarization response--is introduced: the polarimetric texture signature. The second moment of backscattered power characterizes statistically the variation of the radar signal due to speckle and the underlying radar cross section. The classical texture product model with a scalar radar reflectivity implies that the normalized second moment of intensity does not depend on the polarization state. However, such dependency is found in experimental observations, a fact that calls for further investigation of the phenomenon. Considering at first speckle statistics for homogeneous areas having no texture, it is demonstrated that correlation among the single-look speckle patterns, which are added on an intensity basis in a multilook operation, is responsible for a weak polarization dependency of the normalized second moment. Concerning the textural properties, a new model is proposed--the mixture model--where it is assumed that polarimetrically diverse scattering mechanisms contribute to the total return from an ensemble of resolution elements. Numerical simulations are used to reconstruct the texture signatures according to the mixture model, starting from simple assumptions related to scattering processes from natural targets. It is found that the polarimetric texture signature can be an interesting discriminator of weak targets against clutter, when only polarimetric diversity and not radiometric diversity plays a role. The effects predicted by the theory are confirmed by experimental analysis of polarimetric data acquired by the Jet Propulsion Laboratory AIRSAR sensor. Finally a classification scheme based on the polarimetric texture signature is proposed. Index Terms--Polarimetry, synthetic aperture radar (SAR) statistics, speckle, texture.

In this paper, the circular polarization coherence, [rho]RRLL, is investigated as a potential estimator of terrain surface roughness and small-scale slopes. The studies utilize microwave backscatter collected from 1) dielectric surfaces in an anechoic chamber and 2) a desert test site using P-, L-, and C-band NASA/JPL AIRSAR data. These experimental studies and supporting theory, indicate a sensitive decrease of |[rho]RRLL| with increasing surface roughness ks over a range 0 [less than or equal to] ks [less than or equal to] 1. For the present studies this decrease is caused largely by the depolarizing effects of small-scale surface slopes in the azimuth direction rather than by volume, or multiple scatter. For cases when the scatter is reflection symmetric, the value of |[rho]RRLL| depends on the surface roughness and on the local incidence angle. The dependence of |[rho]RRLL| on the local incidence angle is supported by theory and experimental results. For these same scattering cases, however, |[rho]RRLL| is independent of the surface dielectric constant. Estimation of the functional dependency of |[rho]RRLL| versus ks, for a mid-range incidence angle, has been carried out using roughness estimates derived from an empirical model. Further studies, involving more incidence angles, are required to develop a complete model to invert the data. Additional results show that the Re[[rho]RRLL] is often preferable over |[rho]RRLL| as a roughness estimator when scatter occurs from vegetated areas. A comparison between Re[[rho]RRLL] and a related polarimetric parameter, the anisotropy A, indicates that for scatter having k8 [less than or equal to] 0.5, Re[[rho]RRLL] is a more sensitive estimator of surface roughness. Index Terms--Radar polarimetry, slopes, surface roughness, synthetic aperature radar (SAR).

In recent studies, Schuler et al. [4] applied polarimetric imaging radar-derived orientation angles to measure topography, and Lee et al. [2] used orientation angles for polarimetric SAR data compensation, to ensure accurate estimation of geophysical parameters in rugged terrain areas. To support these applications, it is important to accurately estimate shifts in orientation angles induced by the azimuth slope variations. However, in many cases, inconsistency in the estimation of orientation angle shifts was encountered in several areas, introducing noisy and erroneous results. In this paper, we develop a unified analysis of estimation algorithms based on the circular polarization covariance matrix. The concept of reflection symmetry is used to explain the soundness of the circular polarization method and to show problems associated with other algorithms. L-band polarimetric synthetic aperture radar (SAR) images of Camp Roberts, CA, are used to substantiate this theory. Index Terms--Radar polarimetry, synthetic aperture radar (SAR).

In this paper, we introduce a new classification scheme for dual frequency polarimetric SAR data sets. A (6 x 6) polarimetric coherency matrix is defined to simultaneously take into account the full polarimetric information from both images. This matrix is composed of the two coherency matrices and their cross-correlation. A decomposition theorem is applied to both images to obtain 64 initial clusters based on their scattering characteristics. The data sets are then classified by an iterative algorithm based on a complex Wishart density function of the 6 x 6 matrix. A class number reduction technique is then applied on the 64 resulting clusters to improve the efficiency of the interpretation and representation of each class. An alternative technique is also proposed which introduces the polarimetric cross-correlation information to refine the results of classification to a small number of clusters using the conditional probability of the cross-correlation matrix. These classification schemes are applied to full polarimetric P, L, and C-band SAR images of the Nezer Forest, France, acquired by the NASA/JPL AIRSAR Sensor in 1989. Index Terms--Multivariate statistics, radar polarimetry, synthetic aperture radar (SAR), terrain classification.

This paper addresses the land-use classification capabilities of fully polarimetric synthetic aperture radar (SAR) versus dual-polarization and single-polarization SAR for P-, L-, and C-Band frequencies. A variety of polarization combinations will be investigated for application to crop and tree age classification. Based on the complex Wishart distribution for the covariance matrix, maximum likelihood (ML) classifiers for all polarization combinations were used to assess quantitative classification accuracy. Thus, this allows optimally selecting the frequency and the combination of polarizations for various applications. Index Terms--Radar polarimetry, synthetic aperture radar (SAR), terrain classification.

Using simulations of radar cross section (RCS) based on wavecurrent interaction calculations, we investigate the origin of a prominent enhancement in L-band from signals that were transmitted and received, respectively, with horizontal (H) and vertical (V) polarization radar return. This was observed in imagery of the northern boundary of the Gulf Stream (GS) during the first Shuttle Radar Laboratory (SRL-1) mission. The calculations of surface roughness are based on a one-dimensional (l-D) surface current model that closely resembles a current shear that was observed in in situ current measurements, taken at both sides of the GS at the time SRL-1 imaged the GS boundary. In agreement with trends observed in the imagery, significant enhancements in L-band HV polarization cross section occur in the neighborhood of the GS thermal boundary, relative to comparable vertical polarization (VV) cross section signatures at X-, C-, and L-band. We also find reasonably good agreement between the simulated and observed magnitudes of the GS signatures (based on calculations of wave action) using two different radar imaging models, and we provide an overview of a number of additional submesoscale features associated with the GS that were present in the image of the GS boundary. Index Terms: Radar cross section, scattering, synthetic aperture radar (SAR).

This paper proposes a new approach in polarimetric synthetic aperture radar (SAR) speckle filtering. The new approach emphasizes preserving polarimetric properties and statistical correlation between channels, not introducing crosstalk, and not degrading the image quality. In the last decade, speckle reduction of polarimetric SAR imagery has been studied using several different approaches. All of these approaches exploited the degree of statistical independence between linear polarization channels. The preservation of polarimetric properties and statistical characteristics such as correlation between channels were not carefully addressed. To avoid crosstalk, each element of the covariance matrix must be filtered independently. This rules out current methods of polarimetric SAR filtering. To preserve the polarimetric signature, each element of the covariance matrix should be filtered in a way similar to multilook processing by averaging the covariance matrix of neighboring pixels. However, this must be done without the deficiency of smearing the edges, which degrades image quality and corrupts polarimetric properties. The proposed polarimetric SAR filter uses edge-aligned nonsquare windows and applies the local statistics filter. The impact of using this polarimetric speckle filtering on terrain classification is quite dramatic in boosting classification performance. Airborne polarimetric radar images are used for illustration. Index Terms: Polarimetric SAR, polarimetric SAR speckle filtering, terrain classification.

In this paper, we propose a new method for unsupervised classification of terrain types and man-made objects using polarimetric synthetic aperture radar (SAR) data. This technique is a combination of the unsupervised classification based on polarimetric target decomposition [9] and the maximum likelihood classifier based on the complex Wishart distribution for the polarimetric covariance matrix [15]. We use Cloude and Pottier's method to initially classify the polarimetric SAR image. The initial classification map defines training sets for classification based on the Wishart distribution. The classified results are then used to define training sets for the next iteration. Significant improvement has been observed in iteration. The iteration ends when the number of pixels switching classes becomes smaller than a predetermined number or when other criteria are met. We observed that the class centers in the entropy-alpha plane are shifted by each iteration. The final class centers in the entropy-alpha plane are useful for class identification by the scattering mechanism associated with each zone. The advantages of this method are the automated classification, and the interpretation of each class based on scattering mechanism. The effectiveness of this algorithm is demonstrated using a JPL/AIRSAR polarimetric SAR image. Index Terms: Radar polarimetry, synthetic aperture radar, terrain classification

This paper addresses the noise filtering problem for synthetic aperture radar (SAR) interferometric phase images. The phase noise is characterized by an additive noise model. The model is verified with an L-band shuttle imaging radar (SIR)-C interferogram. An adaptive filtering algorithm based on this noise model is developed. It emphasizes filtering noise adaptively according to the local noise level and filtering along fringes using directionally dependent windows. This algorithm is effective, especially for the tightly packed fringes of X-band interferometry. Using simulated and SIR-C/X-SAR repeat-pass generated interferograms, the effectiveness of this filter is demonstrated by its capabilities in residue reduction, adaptive noise filtering, and its ability to filter areas with high fringe rates. In addition, a scheme of incorporating this filtering algorithm in iterative phase unwrapping using a least-squares method is proposed. Index Terms - Interferometric synthetic aperture radar (IF-SAR), phase noise filtering, phase noise modeling, phase unwrapping.

A modified Hough transform based on a likelihood principle of connectivity and thickness is proposed for line detection. It makes short as well as thick line segments easier to detect in a noisy image. Certain desirable properties of the new method are justified by theory and simulations. Index Terms - Hough transform, line detection, pattern recognition, likelihood principle, pixel connectivity.

A radiometric and textural classification method for the single-channel synthetic aperture radar (SAR) image is proposed, which explicitly takes into account the probability density function (pdf) of the underlying cross section for K-distributed images. This method makes extensive use of adaptive preprocessing methods (e.g., Gamma-Gamma MAP speckle filtering), in order to ensure good classification accuracy as well as fair preservation of the spatial resolution of the final result. Error rates can be estimated during the training step, allowing one to select only relevant reflectivity classes and to save computation time in trials. The classification method is based on a Maximum Likelihood (ML) segmentation of the filtered image. Finally, a texture criterion is introduced to improve the accuracy of the classification result.

A processing technique for polarimetric synthetic aperture radar (SAR) data has been developed which produces profiles of terrain slopes and elevations in the azimuthal (or along-track) direction. This technique estimates the average shift in orientation angle of copolarization backscatter caused by azimuthal tilts of the scattering plane. Using P-band data, tests of this technique have been made for an area in the Black Forest near Villingen/Schwenningen in Baden-Wurttemberg, Germany. The radar measured slope and derived elevation profiles have low rms errors and high correlation values when compared with a stereo-photograph digital-elevation map (DEM) for the area. This algorithm is capable of adaptively making transitions from the forested areas to nearby regions with open-terrain. Subsequent tests of the algorithm have been conducted using polarimetric SAR L-band data for a mountainous, nonforested, region in the Mojave Desert (Ft. Irwin, CA) where an accurate DEM also was available. Complete elevation and slope mapping of the terrain in two dimensions using this technique is possible when azimuthal elevation profiles are produced throughout the range extent of the SAR image.

We demonstrate the ability of interferometric radar imagery to determine both relative and absolute surface velocities in the open ocean. Absolute phase calibration is accomplished by noting the azimuthal displacement of range-travelling targets - demonstrating for the first time that under favorable circumstances phase calibration can be achieved in open-ocean in the absence of ground truth. The high resolution of radar imagery permits observation of sharp velocity discontinuities, e.g. the Gulf Stream boundary and the wave field. The recent SIR-C/X-SAR shuttle missions dramatically emphasize the experimental and observational aspects of space-based radar. The combination of absolute velocities, high spatial resolution, and wide-area coverage suggest that interferometric radar imagery can provide a unique and powerful aid both for studies of global circulation patterns and detailed analysis of slope/shelf water interactions with ocean currents. In particular, we employ this measurement of the surface currents and wave field near a velocity front to help refine and bound results of our modeling of calculated radar images of the front. The results of this paper are compared with available ground truth.

Polarimetric and interferometric SAR data are frequently multilook processed fo speckle reduction and data compression. The statistical characteristics of multilook data are quite different from those of single-look data. In this paper, we investigate the statistics of their intensity and phase. Probability density function (PDF's) of the multilook phase difference, magnitude of comple product, and intensity and amplitude ratios between two components of the scattering matrix are derived, and expressed in closed forms. The PDF's depend on the complex correlation coefficient and the number of looks. Comparisons of these theoretically derived PDF's are made to measurements from NASA/JPL AIRSAR data. The results of this paper can be applied to feature classification using polarimetric SAR and to the estimation of decorrelation effects of the interferometric SAR.

This work is motivated by ship identification from a sequence of ISAR images. Maximum likelihood classification, based on point matching, is formulated when the observed images are subject to missing points and phantoms. The 3-D to 2-D transformation is assumed to be known only in a certain parametric form. Proper weights, based on the noise levels for all images, are derived for the classification formula. The new formulation simplifies the computation of matching and makes its extension to object identification from multiple images feasible. Moreover, some theoretical properties of the identification procedure can now be investigated. Guidelines on which groups of objects are easier to distinguish are found from statistical theory followed by intuitive explanation. This method is then applied to ship identification with simulated ISAR images.

A generalized principal components transform (PCT) is developed which maximizes the signal-to-noise ratio (SNR) and which tailors to the multiplicative speckle noise characteristics of polarimetric SAR images. An implementation procedure which accurately estimates the signal and the noise covariance matrices is established. The properties of the eigenvalues and eigenvectors are investigated, revealing that the eigenvectors are not orthogonal, but the principal component images are statistically uncorrelated. Both amplitude (or intensity) and phase difference images are included for the PCT computation. The NASA/JPL polarimetric SAR imagery of P, L, and C bands and quadpolarizations is used for illustration. The capability of this principal components transformation in information compression and speckle reduction enables automated image segmentation and better human interpretation.

This paper addresses the problem of polarimetric SAR (POLSAR) data correction for changes in radar cross sections, which are caused by azimuth slopes. Most radiometric slope corrections remove slope effects to account for the effective scattering pixel area. However, few studies address the slope effect on the radar cross section as a function of polarization states. We propose two approaches to compensate for this azimuth slope effect for POLSAR data. In the first approach, the digital elevation model (DEM), obtained from interferometric SAR or from other means, is used to estimate orientation angles, and in the second approach, orientation angles are derived directly from the POLSAR data. We have developed three new methods to implement this second approach. One is based on the right--right and left--left circular polarizations, and the other two are based on Cloude's and Huynen's decompositions. The results are compared with the original polarization signature method using the DEM-generated orientation angles as the reference. POLSAR data is then compensated using the derived orientation angles. Significant changes were observed in many elements of the coherency matrix. The compensated POLSAR data should improve the accuracy of geophysical parameter estimation techniques. NASA/JPL AIRSAR data for an area in central California is used for illustration. Index Terms--Data compensation, radar polarimetry, synthetic aperture radar (SAR).