Model-based polarimetric decompositions are often used to generate scene classifications from polarimetric synthetic aperture radar (POLSAR) imagery. The classification quality largely depends on whether the adopted polarimetric scattering mechanisms match the major in-scene scattering mechanisms. However, in-scene scattering variations still cause potential mismatches, resulting in unphysical or inaccurate decomposition results. The robustness of model-based polarimetric decompositions remains a general concern. In this article, we address the robustness of model-based polarimetric decomposition to variations of the in-scene scattering mechanisms using simulated datasets. The known simulated scattering mechanisms provide the needed “ground truth” against which we quantitatively evaluate the robustness of polarimetric decompositions. Accurate retrieval of known scattering mechanisms from simulated polarimetric data led us to develop a robust model-based decomposition. We propose a new approach to solve model-based decomposition by employing an$L_{1}$-regularized optimization procedure, which automatically selects a set of optimal polarimetric scattering mechanisms and guarantees nonnegative powers for the selected scattering mechanisms. We illustrate this$L_{1}$model decomposition by employing both simulated datasets and actual POLSAR imagery. Our new$L_{1}$-regularized approach to POLSAR model-based decomposition that mitigates observed biases seen in earlier decompositions provides robust scattering mechanism estimates and eliminates unphysical, negative scattering powers.

Polarimetric SAR (PolSAR) imagery offers an enhanced capability to reveal the salient scattering properties of scene content. PolSAR-based target decomposition has been widely used to show different apparent scattering mechanisms for various target classes, empowering a direct yet powerful technique for SAR imagery analysis. Among those common targets, modeling the random volume scattering from vegetation is one of the most important. Generally, one models vegetation as a cloud of randomly oriented thin cylinders, mainly intended for twigs and branches. At high radar frequencies, PolSAR imagery shows a strong response from leaves in the vegetation canopy. In this letter, we derive the polarimetric scattering theory for general random volume scatterers, including both thin cylinders and thin disks as limiting cases for leaf response. Adding the proposed random thin disk model explains the observed scattering difference between deciduous forest and coniferous forest, which we then incorporate into a new model-based PolSAR target decomposition scheme.

The effectiveness of refined interferometric synthetic aperture radar (InSAR) filter (refined filter) was validated using both simulated and real interferometric data. However, the threshold as a key parameter in the refined filter is determined by repeated attempts, and the deterministic method has some disadvantages, such as strong subjectivity, long time consuming, and bad adaptation. This letter makes full use of the robustness of inverse distance weighting (IDW) to simplify the refined filter. In the proposed filter, using an 11 × 11 window and an improved IDW, the computational burden of I(ejψz)I for 16 windows per pixel is avoided, and the need for setting the threshold is eliminated. From the beginning, the initial window angle is determined using four preprocessing windows. The filtering direction of the center pixel is affected by the initial window angle of each pixel, the degree of homogeneity of each pixel, and the distance of the pixel from the center pixel in the 11 × 11 window. The improved weighting method is then used to further refine the window angle of the center pixel. Numerical experiments by simulated and real InSAR data were used to validate the proposed approach. By comparison, the proposed filter greatly improves the efficiency of the refined filter and exhibits the superiority in filtering performance compared to commonly used filters, particularly for regions of low coherence, high-coherence-gradient, and high-phase gradient.

Model-based polarimetric decompositions are often used to generate scene classifications from polarimetric SAR imagery. The original Freeman-Durden model has been modified and improved upon multiple times over the past 2 decades. However, quantitative, in-depth analyses of these incoherent model-based decompositions have lagged in comparison. Here we assess model-based polarimetric decomposition techniques for robustness to variations of the in-scene scattering mechanisms using simulated data sets. The simulated scattering mechanisms, e.g. volume scattering model, polarimetric signal-to-noise, simple multi-bounce models, etc., are known and thus provide "ground truth." We will illustrate our results by applying select model-based decompositions to both simulated and actual polarimetric SAR imagery.

Steep terrain presents a challenge in soil moisture estimation. In this paper, we illustrate the necessity of polarization orientation angle compensation for soil moisture estimation based on Bragg scattering. We also emphasize the importance of local incidence angle for soil moisture estimation. We found that azimuth slopes cause underestimation, but range slopes can cause over- or underestimated soil moisture depending on range slopes being positive or negative. Theoretically, if both azimuth and range slope measurements are available, soil moisture level can be retrieved precisely. However, to achieve this, a coregistered DEM is indispensable.

Bistatic synthetic aperture radar (BiSAR) offers flexible imaging configurations to form SAR images at various sensing geometries. However, polarimetric observations with BiSAR are also subject to greater geometrical dependency compared to those with monostatic SAR. The polarimetric analysis practical to retrieve geophysical target parameters is currently lacking on the BiSAR imagery. In this paper, we investigate its dependence on the bistatic geometry using both isotropic and anisotropic targets. It shows that for general targets the bistatic geometry convolves with target shape, orientation and polarization angles in all polarimetric parameters.

Model-based polarimetric decompositions have long been applied to classify polarimetric SAR imagery. This long and successful history has, at times, been interrupted by nagging issues: polarimetric orientation angle effects, negative scattering powers, simplistic scattering models, etc. We test model-based polarimetric decomposition techniques for robustness to variations of the underlying in-scene scattering mechanisms. We determine the accuracy and robustness of the decomposition results using simulated data sets. The simulation inputs, e.g. volume scattering model, polarimetric signal-to-noise, simple multi-bounce models, etc., are known and thus provide “ground truth” for comparison to model-based decomposition results. We will illustrate our results by applying a select set of model-based decompositions to both simulated and actual PolSAR imagery.

In this paper, we address polarimetric radar scattering from steep terrain and its relation to polarization orientation angle (POA) by adopting a tilted surface model based on Bragg scattering. We found that, as the azimuthal slope increases, |VV| decreases at a faster rate than |HH|, they become equal, when POA is ±45°, and |HH|>|VV| afterwards. Contrasting to the general perception, the cross-pol |HV| does not always increase with azimuth slope, but reaches a maximum then decreases to zero. In addition, we investigate the effect of soil moisture on PolSAR scattering characteristics of steep terrain and the effect of vegetation over surface on POA estimation. The latter is demonstrated with NASA/JPL TOPSAR L-band PolSAR data and C-band InSAR data.

Pixels in SAR imagery can be grouped as those of coherent scatterers and those of distributed scatterers. Identifying coherent scatterers from distributed scatterers is of a general interest because the former are often associated with man-made targets whereas the latter are often associated with natural clutter. An ideal coherent scatter would exhibit scattering stability with respect to time, frequency, aperture angle, and polarization state. At high resolution, coherent scatterer returns may not satisfy the premise of circularly complex speckle. In this paper, we compare and evaluate the coherent scatterer detection methods based on the spectral stability among multiple sub-bands and one based on the non-circularity metric. It shows evaluating non-circularity offers a favorable option for identifying coherent scatters and accordingly demonstrates the need to be cautious on the circular speckle premise in high resolution images.

Polarization orientation angle (POA) is an important parameter of polarimetric radar scattering from slopes in mountainous region. It is known that surface tilted in azimuth direction and buildings not aligned in the along-track direction induce polarization orientation shifts. Earlier research has established orientation angle as a function of radar imaging geometry and surface slopes, and that POA estimation can be derived from polarimetric radar data using circular polarization. Besides these, polarimetric scattering from steep slopes and its relation to POA remain not well understood. In this paper, we address these issues by adopting a tilted surface model based on Bragg scattering. We have found that, as the azimuthal slope increases, |VV| decreases at a faster rate than |HH|, they become equal, when POA is ±45°, and |HH| > |VV| afterward. In other words, the Pauli component, |HH-VV| reduced to zero at POA = ± 45°, and the typical Bragg scattering characteristics of |VV| > |HH| does not apply when steep slope is present inducing |POA| > 45°. Furthermore, the cross-pol |HV| does not always increase with azimuth slope but also reaches a maximum then decreases to zero. In addition, we investigate the effect of soil moisture on polarimetric SAR (PolSAR) scattering characteristics of steep terrain and the effect of vegetation over surface on POA estimation. The latter is demonstrated with NASA/JPL TOPSAR L-band PolSAR data and C-band InSAR data. Another significance of this paper is that it provides a direct and rigorous derivation of POA equations. The earlier version was derived from a different concept.

We propose to test model-based polarimetric decomposition techniques for robustness to variations in the underlying in-scene scattering mechanisms. The accuracy and robustness of the decomposition results are determined from simulated data sets. The simulation input parameters, e.g. volume scattering model, polarimetric signal-to-noise, etc., are known and thus provide the “ground truth” for comparison to the model results. The observed correlations between simulation and model results allow estimates of model accuracy and model robustness.

In this paper, the recent advancement of PolSAR filtering is reviewed. Speckle filtering is a necessary procedure for most of PolSAR applications, to reduce speckle noise level and to form incoherent scattering by averaging coherency or covariance matrices of neighboring pixels. In this paper, we addressed several important but often ignored issues associated with PolSAR speckle filtering, and the recent very high resolution SLC PolSAR data demands special consideration in speckle filtering. The conclusion, we have reached, is that the best filter does not exist, because the desired filtering is determined by application requirements and personnel preference. A filter may be good for one application, but could be undesirable for other applications. In other words, speckle filtering is not an exact science, and the perfect speckle filtering cannot be achieved.

In high-resolution polarimetric synthetic aperture radar (SAR) images, texture variation increasingly dominates the scene of those selected targets, creating a major challenge in speckle modeling and target classification. When texture is incorporated into speckle models, it is often assumed that pixels are independent from each other. In this paper, we allow dependent neighboring pixels and evaluate the texture as a Markov random field (MRF). The estimated local dependence potentially reveals the spatial patterns of targets which are used to augment polarimetric SAR image classification through a MRF-mixture model. We evaluated the MRF-mixture model to describe textured speckle and performed a classification experiment on a high-resolution image. It shows an improvement when heterogeneity and spatial patterns are clearly present.

Speckle filtering is an indispensable operation in synthetic aperture radar (SAR) image processing but one which inevitably reduces image resolution. In order to preserve the intrinsic target features, adaptive speckle filters have been developed using weighted averages commensurate with the similarity of the target statistics. The target statistics are commonly derived from a prefiltering step which suffers from residue speckle contamination and feature smearing. In this paper, we adopted finite mixture models to characterize the observed in-scene variation and proposed a rigorous and progressive mixture regression method to better estimate the target statistics. The mixture model once fitted is able to capture the statistical properties of the highly textured and heterogeneous target variation, which is often observed in high-resolution SAR images. A nonlocal mean method is used for robust similarity evaluation of the local variation patterns between small image patches. The goal is to develop an improved polarimetric SAR (PolSAR) speckle filter that can accomplish a solid balance between speckle suppression and feature preservation. With the proposed filter, distinct scattering mechanisms and small-scale target features are retained from the start, even with single-look complex PolSAR observations. We test the algorithm using simulated data and single-look high-resolution PolSAR images: one acquired by DLR's F-SAR system and one by DLR's E-SAR system.

In this paper, we investigate the double bounce radar backscattering from building walls not aligned in the azimuth direction (i.e., slanted double bounce scattering) based on scattering models. We apply the Fresnel equation for reflections from the building wall, and then the bistatic small perturbation model developed by Ulaby for ground scattering. Our analysis indicates that scattering matrix is not symmetrical for the path from radar to wall to ground and back to radar, but adding the reverse path from radar to ground to wall and back to radar makes the scattering matrix symmetrical. The effect on the polarization orientation estimation for an urban scene using L-band E-SAR will be analyzed.

At high resolution, synthetic aperture radar (SAR) speckle tends to be non-Gaussian distributed and diversely textured. Many parametric speckle distributions have been developed to fit specific in-scene content. In contrast, mixture models offer an empirical approximation with the potential to fit arbitrary variations. In this letter, we investigate the feasibility and the efficiency of using finite mixture models of an identical parametric kernel to characterize the wide range of high-resolution speckle. We evaluate and compare the capability of mixture fitting with gamma,$\mathcal{K}$, and$\mathcal{G}^{0}$kernels against various scene types. Despite the characterization disparity among these base kernels, we show that using any of them in a mixture setting rapidly improves speckle modeling. Finite gamma mixtures, even with a simple kernel form, are applicable to high-resolution SAR imagery for consistent description of complex textured speckle variations.

The advancement of synthetic aperture radar (SAR) technology with high-resolution and quad-polarization data demands better and efficient polarimetric SAR (PolSAR) speckle-filtering algorithms. Two requirements on PolSAR speckle filtering are proposed: 1) speckle filtering should be applied to distributed media only, and strong hard targets should be kept unfiltered; and 2) scattering mechanism preservation should be taken into consideration, in addition to speckle reduction. The purpose of this paper is twofold: 1) to propose an effective algorithm that is an extension of the improved sigma filter developed for single-polarization SAR; and 2) to investigate speckle characteristics and the need for speckle filtering for very high resolution (decimeter) PolSAR data. The proposed filter was specifically developed to account for the aforementioned two requirements. Its effectiveness is demonstrated with Jet Propulsion Laboratory airborne synthetic aperture radar data, and comparisons are made with a boxcar filter, the refined Lee filter, and a Wishart-based nonlocal filter. For very high resolution PolSAR systems, such as the German Aerospace Center F-SAR and Japanese Pi-SAR2, with decimeter spatial resolution, we found that the complex Wishart distribution is still valid to describe PolSAR speckle characteristics of distributed media and that speckle filtering may be needed depending on the size of objects to be analyzed. F-SAR X-band data with 25-cm resolution is used for illustration.

The complexity of polarization orientation angle induced by scatterings from buildings and distributed media will be investigated. We will look into 1) the estimation algorithm, 2) The double bounce scattering from building walls not aligned in the along-track direction and 3) the orientation angle associated with very high-resolution PolSAR images with decimeter resolution.

We introduce a polarimetric SAR speckle filter by combining finite mixture distributions and the so-called non-local filtering scheme. The finite mixture distributions are used to describe the statistical properties of the highly textured, heterogeneous scene in high-resolution SAR images. The non-local filtering scheme is used to test similarity in the local patterns to include sufficient pixels for better speckle reduction. The filter is capable of retaining distinctive scattering mechanisms from the onset. The improvement is demonstrated using DLR's high-resolution F-SAR images.

We have performed simulations of random rough surface scattering using 3-D numerical solution of Maxwell equations (NMM3D) using surface size up to 32 × 32 squared wavelengths. The rough surfaces are characterized by exponential correlation functions. The simulation results of crossand copolarization backscattering coefficients were in good agreement with experimental measurements of bare soils at L-band. Because in numerical solutions of Maxwell equations the electric fields of the scattered wave are calculated for each realization, scattering matrices can be simulated by NMM3D, and such simulations are performed in this paper. For a given RMS height, correlation length, soil permittivity, and incident angle, we calculated the radar scattering matrix up to 958 independent realizations. For each realization, the components of the scattering matrix, namely, SHH, SVV, SHV, and SVH, are calculated. Using the simulated scattering matrices, we calculate the polarimetric speckle statistics (amplitude and phase difference), followed by a comparison with theoretical distributions. For fully developed speckle from the homogeneous rough surface, the results are examined and validated to ensure the simulated data quality as far as polarimetric properties are concerned. By taking ensemble averages, we calculate the coherency matrix from which the eigenvalues, entropy, anisotropy, and alpha angle in coherent target decomposition are then calculated. In particular, characterization of polarimetric descriptors for rough surface is presented. Issues of scattering symmetry characteristics are also discussed.

The model-based scattering decomposition pioneered by Freeman and Durden has stimulated research in characterizing polarimetric synthetic aperture radar (PolSAR) scattering phenomenon and its applications. The Freeman and Durden decomposition as originally developed is based on three scattering models for volume, surface, and double bounce. It is known that the decomposition often produces negative scattering powers for a large number of pixels. This implies that the scattering models are inconsistent with the data. In this paper, we investigate the model deficiency problem and propose several algorithms to mitigate it. To achieve this, we developed an incoherent scattering model based on the polarization orientation angle distribution of phase differences. Two approaches are taken to reduce the number of negative power pixels: 1) adopt a volume scattering model by including variable shape factor while keeping the original surface and double bounce models unchanged, and 2) incorporate the incoherent surface or double bounce model while keeping the original volume model. In addition, the combination of 1) and 2) is explored, and the effect of polarization orientation compensation on these algorithms is investigated. The effectiveness of these approaches is compared using L-band AIRSAR and E-SAR PolSAR data. It will be shown that model efficiency is improved with occurrence of negative power reduced to an insignificant level.

Radar interferometry has been widely applied in measuring the terrain height and its changes. The information about surface can be derived from phase interferograms. However, the inherent phase noise reduces the accuracy and reliability of that information. Hence, the minimization of phase noise is essential prior to the retrieval of surface information that is embedded in an interferometric phase. This paper presents a refined filter based on the Lee adaptive complex filter and the improved sigma filter that was originally developed for amplitude image filtering. The basic idea is to adaptively filter the interferometric phase according to the local noise level to minimize the loss of signal for a particular pattern of fringes, including such extreme cases as involving broken fringes, following the removal of undesired pixels. Ultimately, the goals are to preserve the fringe pattern, to reduce phase bias and deviation, to reduce the number of residues, and to minimize the phase error. The preservation of the fringe pattern is particularly of concern in areas of high frequency of fringe and large phase gradient corresponding to steep terrains. The proposed refined filter was validated using both simulated data and real interferometric data. Results demonstrate that the filtering performance is better than that of commonly used filters.

Radar interferometry has been widely applied in measuring the surface height. The information about surface can be derived from phase interferograms. However, phase noise reduces the accuracy and reliability of that information. Hence, the minimization of phase noise is essential to the retrieval of surface information. This work presents a refined filter that is based on the Lee adaptive INSAR filter and the sigma filter. The basic idea is to filter adaptively the interferometric phase according to the local noise level to minimize the loss of signal for a particular shape of fringes, including in such extreme cases as involve broken fringes, following the elimination of unreliable pixels of phase noise. The goal is to reduce the phase deviation and the number of residues, and minimize the phase error. The proposed filter was inspected herein using both simulated data and real interferometer data. Results reveal that the filtering performance is better than that of commonly used filters.

The advancement of SAR technology with high resolution and quad-polarization data demands better and efficient speckle filtering algorithms. In principle, filtering should be applied to distributed media only. Strong pointed targets should be kept unfiltered. In this paper, we will review some of the recently published PolSAR speckle filtering algorithms, and discuss several important issues that affect the speckle filtering results.

The Freeman and Durden decomposition as original developed is based on three scattering models for volume, surface and double bounce. It is known that the decomposition produces negative scattering powers for a large number of pixels. This implies that the scattering models are inconsistent with the data. In this paper, we investigate the negative power problem and propose two algorithms to mitigate it. To achieve this, alternative scattering models have been adopted, and new families of incoherent scattering models are developed. The effectiveness of these approaches is compared using L-band E-SAR polarimetric data. It will be shown that the negative power problem can be reduced to an insignificant level.

A feature space for scatterer characterization is constructed of the geophysical parameters of scatterers on size, shape, and orientation. Dense divisions are defined to discriminate target classes of possibly subtle distinction. The statistical similarity and uncertainty of overlapping cluster pairs are evaluated with Wishart based likelihood ratio and at a desired level of false classification the dense set of clusters is hierarchically pruned. Wishart based classification is then applied to the whole imagery, accomplishing a physical-based unsupervised classification algorithm. The algorithm is illustrated using an AIRSAR dataset of San Francisco to evaluate its capability in characterizing complex terrains. As an optional step, the K-Means or Expectation-Maximization iteration is performed to further adapt the cluster centers.

In this paper, we propose a terrain categorization algorithm for single-polarization high-resolution SAR imagery, and coded the terrain categories by colors that resemble Pauli decomposition color coding based on scattering mechanisms of PolSAR data. The proposed algorithm emphasizes preserving high resolution and making information dissemination easier for single-pol SAR data.

In the last decade, the three-component polarimetric scattering decomposition by Freeman and Durden has stimulated research and applications in model-based decompositions. In this paper, we will review recent advances in the scattering models, focusing on the negative power problem and its mitigation, the problem of the reflection symmetry assumption, and the effect of orientation angle compensation. An extended decomposition based on the extended Bragg model is also proposed. Polarimetric SAR images illustrate our approach.

In this paper, we set to investigate the orientation and structure parameters retrieved from POLSAR observations for canopy scattering and evaluate their capability to improving physical retrievals for vegetation and forest targets. Both the orientation and structure parameters of the canopy can be described as a cloud of spheroids with independent orientation distribution and shape distribution. The respective parameters express themselves in a clearer form in the circular polarization basis. The findings on the orientation and structure parameters are used to assist Wishart based polarimetric classification.

The polarization orientation angle (OA) of the scattering media affects the polarimetric radar signatures. This paper investigates the effects of orientation compensation on the coherency matrix and the scattering-model-based decompositions by Freeman-Durden and Yamaguchi et al. The Cloude and Pottier decomposition is excluded, because entropy, anisotropy, and alpha angle are roll invariant. We will show that, after orientation compensation, the volume scattering power is consistently decreased, while the double-bounce power has increased. The surface scattering power is relatively unchanged, and the helicity power is roll invariant. All of these characteristics can be explained by the compensation effect on the nine elements of the coherency matrix. In particular, after compensation, the real part of the (HH - VV) · HV* correlation reduces to zero, the intensity of cross-pol |HV| always reduces, and |HH - VV| always increases. This analysis also reveals that the common perception that OA compensation would make a reflection asymmetrical medium completely reflection symmetric is incorrect and that, contrary to the general perception, the four-component decomposition does not use the complete information of the coherency matrix. Only six quantities are included - one more than the Freeman-Durden decomposition, which explicitly assumes reflection symmetry.

The quality of polarimetric synthetic aperture radar (PolSAR) imagery and its polarimetric decompositions depends on the accuracy of polarimetric observations of the SAR system and its calibration. Polarization distortions on the polarimetric measurement can be incurred due to nonideal system polarization quality and propagation factors, such as channel imbalance, crosstalk, and Faraday rotation at lower frequencies. All these distortions have varying impacts on different target types as well as different decomposition methods. In this paper, we assess the polarization quality of the PolSAR system in the context of polarimetric imagery analysis and quantify the various effects of polarization distortions on polarization target decompositions. A generic metric is defined to measure the polarization purity of the system. Considering the fact that target decomposition plays an important role in imagery analysis, we apply several widely used decomposition methods to showcase the polarimetric system requirement based on the defined metric. We demonstrate that this metric can be used for radar system design and polarimetric data calibration.

The 29 papers in this special issue cover the most topical research areas in SAR techniques and technologies, such as bistatic SAR, innovative system concepts, polarimetric systems and data analysis, polarimetric SAR interferometry, and advanced processing techniques for image generation and information extraction.

Recent advances in Polarimetric SAR information extractions are reviewed. Papers published in IEEE Transactions on Geoscience and Remote Sensing and IGARSS proceedings over the last five years were included. We found that PolSAR technology has reached a certain degree of maturity. The availability of high-resolution multi-frequency PolSAR data from space borne and airborne SAR systems will stimulate significant PolSAR applications.

This paper addresses the feasibility of using RADARSAT-2 fine Quad-Pol mode to monitor coastal environment and young tree growth. It will be shown that interferometric coherence may not be high enough for the height estimation of young trees at C-band, but polarimetric sensitivity could be used for tree and crop classification. For coastal environment, we found that polarimetric signature of oyster farm reveals the effect of double bounce scattering and the orientation angle effects.

The quality of polarimetric synthetic aperture radar (PolSAR) imagery and its polarimetric decompositions depends on the accuracy of polarimetric observations of the SAR system and its calibration. Polarization distortions on the polarimetric measurement can be incurred due to nonideal system polarization quality and propagation factors, such as channel imbalance, cross-talk, and Faraday rotation at lower frequencies. All these distortions have varying impacts on different target types as well as different decomposition methods. In this paper, we assess the polarization quality of the PolSAR system in the context of polarimetric imagery analysis and quantify the various effects of polarization distortions on polarization target decompositions. A generic metric is defined to measure the polarization purity of the system. Considering the fact that target decomposition plays an important role in imagery analysis, we apply several widely used decomposition methods to showcase the polarimetric system requirement based on the defined metric.

The orientation angle of scattering media affects the polarimetric radar signatures. This paper investigates the effect of orientation compensation on polarimetric target decompositions including Pauli decomposition, Freeman and Durden decomposition and Yamaguchi decomposition. The Cloude and Pottier decomposition is excluded, because entropy, anisotropy and alpha angle are rotational invariant. We will show that after the orientation compensation, the volume scattering power is consistently decreased, while the double bounce power has increased. The surface scattering power is relatively unchanged, and the helicity power is rotational invariant. All these characteristics can be explained by the compensation effect on the nine elements of the coherency matrix. This analysis reveals that, contrary to the general perception, the 4-component component decomposition by Yamaguichi et al. does not use complete information of the coherency matrix. Only six quantities are included — one more than the Freeman/Durden decomposition under the assumption of refection symmetry.

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.

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 , which show potential to better reconstruct fully polarimetric data.

The advancement of SAR technology with high resolution and multiple polarization data demands better and efficient speckle filtering algorithms. In this paper, we developed an effective speckle filtering algorithm for dual-pol and fully polarimetric high resolution data. The proposed algorithm is effective and computational efficient based on the improved sigma filter [1]. ALOS/PALSAR and JPL AIRSAR data were used for demonstration.

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.

We present a study of the polarimetric information content of dual-pol imaging modes, e.g. (HH, HV) and (VV, VH), and compare these against full quad-pol datasets. We discuss classification accuracies for various dual-pol datasets over several terrain/vegetation types with respect to the full quad-pol classification. The linear polarization modes implemented on space borne SAR systems are of primary importance. However, we also consider novel dual-pol modes, e.g. transmitting a circular polarization and receiving both H and V polarizations, and the so-call compact polarimetry models based on dual-pol data. Our primary emphasis is the inter-comparisons amongst the various dual-pol and compact polarimetric modes.

New space-borne polarimetric SAR systems employ, or will employ, dual-pol imaging modes as well as full quad-pol imaging, e.g. PALSAR, RADARSAT-2, TERRASAR-X. Therefore questions arise as to the capabilities (and limitations) of these various dual-pol SAR imaging modes. One novel offshoot of our work on dual-pol SAR image analysis and polarimetric decompositions of dual-pol data was the idea to collect two dual-pol images employing geometry appropriate for parallel repeat-pass interferometry. The polarizations of the dual-pol collections differ, e.g. one is (HH, HV) and the other (VV, VH). Using the example of transmitting either horizontal (H) or vertical (V) polarizations, one collects all four polarimetric channels, but only two per pass. Except for the interferometric repeat-pass nature of the SAR image collections the polarimetric information should be identical to standard quad-pol SAR imagery. The relevant open questions concern the interferometric baseline and temporal decorrelations between the repeat-pass dual-pol images.

Radar Polarimetry, Radar Interferometry and Polarimetric SAR Interferometry represent the current culmination in active ‘Microwave Remote Sensing’ technology, but we still need to progress considerably more in order to reach the limits of physical realizability. Whereas with radar polarimetry the textural fine-structure, target orientation, symmetries and material constituents can be recovered with considerable improvement above that of standard ‘amplitude-only radar’, by implementing ‘radar interferometry’ the spatial (in depth) structure can be explored. With Polarimetric Interferometric Synthetic Aperture Radar (POL-IN-SAR) imaging, it is possible to recover such co-registered textural and spatial information from POL-IN-SAR digital image data sets simultaneously, including the extraction of Digital Elevation Maps (DEM) from either Polarimetric (scattering matrix) or Interferometric (dual antenna) SAR systems. However, in order to further advance this promising technology, we require pertinent basic educational and advanced research texts plus application oriented books for the practicing engineering scientists and users. Hitherto there are not available satisfactory updated sets of basic books or application oriented handbooks. Therefore, in this paper a succinct review of the existing pertinent monographs, books and guides will be presented with the aim of identifying particular topics that still need to be covered. We do now require these urgently associated sets of revised and updated multi-lingual (English, Japanese, Korean, Chinese) books.

In this paper we investigate the nonstationary behavior of individual polarimetric parameters, e.g. entropy, anisotropy, alpha angle, orientation angle, helicity, etc. We distinguish between parameters that depend solely on the eigenvalues of the standard Cloude-Pottier polarimetric decomposition (span, entropy and anisotropy) and the others that depend on the scattering mechanisms, i.e. the Cloude-Pottier eigenvectors. After producing a series of azimuth subaperture polarimetric images, we apply the polarimetric decomposition to each subaperture frame and identify subaperture frames that show nonstationary scattering. Comparison of several specific targets, both discrete and distributed, will highlight aspects of polarimetric variability with respect to the SAR view angle. We illustrate our results using EMISAR L-band polarimetric SAR data.

Entropy, alpha and anisotropy (H/α/A) of the polarimetric target decomposition has been an effective and popular tool for polarimetric SAR image analysis and geophysical parameter estimation. However, multi-look processing can severely affects the values of these parameters. In this paper, we evaluate the bias problem in H/a/A due to insufficient averaging. We found that the estimated bias is radar frequency dependent. A procedure for bias compensation is proposed. Data from L-band DLR/E-SAR and L-band JPL/AIRSAR, and X-band PI-SAR data are used for demonstration in this study.

The IEM model proposed in 1992, for rough surface scattering has been extensively applied for microwave remote sensing of terrain. Model validation has been made by experimental measurements and by numerical simulations. However, estimation accuracy was verified mostly for like-polarizations in the monostatic configuration. Since then, much effort has been devoted to improving the model performance. A recent AIEM(Advance IEM) incorporated these improvements and demonstrated significant enhancement in accuracy. Analysis was also extended to bistatic scattering for a wide range of surface parameters. In this study, we recognized that both theoretically and experimentally, the modified Stokes vector (Kennaugh matrix), including polarization correlation terms, are very useful to retrieve surface parameters such as wind field. Consequently, we extend the AIEM model to include all polarization correlation terms in order to understand the dependence of four Stokes terms on geophysical surface parameters, such as roughness, correlation length, and dielectric constant. Sensitivity analysis is also performed. Another useful aspect is in the physical interpretation of polarimetric returns as a function of azimuthal angle. Potential applications to soil moisture estimation will be illustrated and discussed.

The IEM model proposed in 1992, for rough surface scattering has been extensively applied for microwave remote sensing of terrain. Model validation has been made by experimental measurements and by numerical simulations. However, estimation accuracy was verified mostly for like- polarizations in the monostatic configuration. Since then, much effort has been devoted to improving the model performance. A recent AIEM(Advance IEM) incorporated these improvements and demonstrated significant enhancement in accuracy. Analysis was also extended to bistatic scattering for a wide range of surface parameters. In this study, we recognized that both theoretically and experimentally, the modified Stokes vector (Kennaugh matrix), including polarization correlation terms, are very useful to retrieve surface parameters such as wind field. Consequently, we extend the AIEM model to include all polarization correlation terms in order to understand the dependence of four Stokes terms on geophysical surface parameters, such as roughness, correlation length, and dielectric constant. Sensitivity analysis is also performed. Another useful aspect is in the physical interpretation of polarimetric returns as a function of azimuthal angle. Potential applications to soil moisture estimation will be illustrated and discussed.

The prevailing complex geological and ecological conditions of Taiwan have drawn considerable attention from various geo-ecological communities because of their vulnerability to produce various natural hazards at different scales. Located in the tropical/subtropical zone of the Pacific Rim, its ecological and rugged mountainous properties are environmentally sensitive making monitoring and observations especially difficult because of the high population density. For example, in terms of natural hazard mitigation tectonically active regions are used for analyzing the cause of abundant risk events, such as earthquakes, landslides and land subsidence. In fact Taiwan is well suited as a test site for studying those geologically disastrous processes. Implementing novel techniques of space remote sensing has proved to be an effective means in recent years for greatly improving our understanding of these phenomena. In this paper we report on the monitoring of such events using multi-modal polarimetric and/or interferometric SAR images at C and L band from ERS, JERS-1, RADARSAT-1, ENVISAT, and from the recent ALOS satellite. For crustal and surface deformation, we used radar image pairs with long temporal baselines and large areas of coverage for investigating deformation over Western Taiwan. Pre-seismic and co-seismic deformation patterns are spatial-temporally analyzed. The other topic deals with the coastline changes observed from a sequence of ERS-1/2 SAR images within the years of 1996 to 2005. Waterlines were extracted using multi-scale procedures of edge detection and were corrected with tidal motion data. Substantial analyses were carried out in conjunction with ground surveys and lidar mapping. The topographic feature changes due to large scale landslides triggered by torrential rains were also monitored. In addition, the SAR interferograms were used to analyze the deposition changes along the riverbeds and riverbanks for short-intervals using optimal baselines. Summary and remarks on the implementation of such multi-modal polarimetric and/or interferometric SAR imagery for environmental monitoring are provided.

The prevailing complex geological and ecological conditions of Taiwan have drawn considerable attention from various geo-ecological communities because of their vulnerability to produce various natural hazards at different scales. Located in the tropical/subtropical zone of the Pacific Rim, its ecological and rugged mountainous properties are environmentally sensitive making monitoring and observations especially difficult because of the high population density. For example, in terms of natural hazard mitigation tectonically active regions are used for analyzing the cause of abundant risk events, such as earthquakes, landslides and land subsidence. In fact Taiwan is well suited as a test site for studying those geologically disastrous processes. Implementing novel techniques of space remote sensing has proved to be an effective means in recent years for greatly improving our understanding of these phenomena. In this paper we report on the monitoring of such events using multi-modal polarimetric and/or interferometric SAR images at C and L band from ERS, JERS-1, RADARSAT-1, ENVISAT, and from the recent ALOS satellite. For crustal and surface deformation, we used radar image pairs with long temporal baselines and large areas of coverage for investigating deformation over Western Taiwan. Pre-seismic and co-seismic deformation patterns are spatial-temporally analyzed. The other topic deals with the coastline changes observed from a sequence of ERS-1/2 SAR images within the years of 1996 to 2005. Waterlines were extracted using multi-scale procedures of edge detection and were corrected with tidal motion data. Substantial analyses were carried out in conjunction with ground surveys and lidar mapping. The topographic feature changes due to large scale landslides triggered by torrential rains were also monitored. In addition, the SAR interferograms were used to analyze the deposition changes along the riverbeds and riverbanks for short-intervals using optimal baselines. Summary and remarks on the implementation of such multi-modal polarimetric and/or interferometric SAR imagery for environmental monitoring are provided.

Theoretical aspects of a technique for target detection and texture segmentation in synthetic aperture radar (SAR) imagery using a wavelet frame are presented. Texture measures consist of multiscale local estimates of the following: 1) normalized second moment of the backscattered intensity and 2) variance of the wavelet-frame coefficients. This work is an extension of a method proposed in the image-processing literature. Novel issues, which are considered in the passage to radar imagery, are the influence of speckle on texture measures afforded by the wavelet frame and their dependence on polarization states (polarimetric texture). Regarding speckle, estimators that decouple the influence of speckle over texture are introduced and characterized by their expected value and variance. The response of the wavelet frame to discontinuities, which is an important issue in target detection problems, is addressed in terms of signal-to-speckle-noise ratio. The notion of polarimetric texture is revisited, providing a theoretical model that explains the dependences of texture measures on the polarization states. For one-point statistics, such model calls for a mixture of diverse polarimetric scattering mechanisms within the texture estimator support. For two-point statistics, the difference in spatial correlation properties among the polarimetric channels is called into play. To analyze these effects in polarimetric SAR data, a novel tool is introduced that is called the Wavelet Polarimetric Signature. The tool encapsulates, in graphical form, the dependence on scale and polarization state of the texture measure afforded by the wavelet frame. The theory exposed here underpins a method that has been proven successful and computationally attractive in a selected number of SAR thematic applications. It also sets the stage for the exploitation of novel target detection and textural segmentation capabilities based on polarimetric diversity.

Entropy, alpha and anisotropy (H/alpha/A) of the polarimetric target decomposition of Cloude and Pettier has been an effective and popular tool for polarimetric SAR image analysis and geophysical parameter estimation. However, multi-look processing can severely affect the values of these parameters. In this paper, a Monte Carlo method is used to evaluate the multi- look effect on these parameters for various media of grass, forest and urban. The effect of pixel correlation due to over sampling, and the mixed pixel effects will also be investigated. DLR/E-SAR and JPL/AIRSAR L-band data are used in this study.

Polarimetric synthetic aperture radar (SAR) backscatter from man-made structures is often quite different than scatter from predominantly natural areas. Backscatter from natural areas is often characterized by near zero values for linear-basis covariance matrix off- diagonal terms of the formHVS*HH> andHVS*VV>. A new approach is proposed to detect man-made structures using circular-pol RR-LL correlation coefficients. This method uses a normalization term, which enhances the return from man-made structures and eliminates most of the unnecessary details of the backscatter from natural areas.

Identification of manmade structures from radar images has always been a difficult task, especially for single-polarization radar. Fully polarimetric radar, however, can provide detailed information on scattering mechanisms that could enable the target or the structure to be identified. Complexity remains stemming from overlaps of single-bounce scattering, double-bounce scattering, and triple- and higher order bounce scattering from various components of a manmade structure, which makes physical interpretation a challenge. In this letter, an interesting example using polarimetric synthetic aperture radar (PolSAR) data of the Great Belt Bridge, Denmark, was presented to illustrate the capability of PolSAR in analyzing radar signatures. Polarimetric target decomposition is used to differentiate the multiple-bounce scatterings contained in the PolSAR images. Two C-band Danish EMISAR data takes, with the first obtained during the bridge's construction and the second after its completion, are used to extract the scattering characteristics of the bridge deck, bridge cables, and supporting structures

Fully polarimetric 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/spl times/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.

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 /spl times/ 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 the POLSAR data. The effect of speckle filtering on scattering characteristics, such as entropy, anisotropy, and alpha angle, will be discussed.

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 multilooking 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.

Polarimetric SAR image classification remains an important research area. Various methods continue to be developed for specific applications. High-resolution polarimetric SAR systems and advances in computational and data storage capabilities have revived interest in novel polarimetric analysis techniques. Accordingly, subaperture analysis of polarimetric SAR data has received renewed attention. A central assumption of SAR image formation is that individual radar scatterers are stationary; they have no structure and provide a constant reflectivity during the imaging process. However, with increased resolution, and hence fewer scatterers per pixel, the nonstationary response from any given scatterer is more likely to influence total radar backscatter of a pixel. We present a method to assess the polarimetric variability across the full aperture.

We reanalyze PacRim 2000 L-band AIRSAR polarimetry collected over the western foothills of central Taiwan a year after the September 20, 1999 ChiChi earthquake, which produced more than 10,000 landslides. The objective is to explore the utility of SAR data, particularly polarimetry, for identifying and mapping natural hazards like landslides that essentially resurface landscapes. An overall goal of this research is to exploit the operational advantages of radars over optical instruments for rapid assessment and response to natural hazards and disasters. Three flightlines of XTI1 AIRSAR were acquired over the two largest landslides, Tsaoling and Mt. Juo-feng-err, as well as hundreds of smaller slides generated by the ChiChi main and aftershocks. We first looked at the relative utility of single channel (co-polarized) SAR, dual polarimetry (co- and cross-polarized channels), and full polarimetry for landslide identification. We found that landslides are very difficult to recognize in fixed polarization SAR data. On the other hand, recognizing hillsides cleared of vegetation by landslides is relatively easy with dual and fully polarimetric data by using cross-polarized and co-polarized cross-sections to provide radar vegetation indices. Radar target decomposition methods applied to the L-band polarimetry allowed us to explore the changes in scattering mechanisms induced by the action of landsliding on forested hillslopes, and to use derived polarimetric parameters to identify landslides. Application of speckle reduction filters does not degrade the resolution with which small landslides can be mapped. Finally, we explored unsupervised classification methods for efficient mapping of landslides under a rapid response mode of operation.

Abstract : Surface can be characterized in terms of its material (dielectric) and geometric properties. The dielectric properties of the surface are expressed primarily by its moisture content, while the roughness describes the geometric characteristics of surface. Various techniques for information retrieval from remotely sensed data have been proposed in a number of recent studies. Some of them are based on an empirical relationship between the measured return signals and the ground truth. Because of their development from a limited number of observations, these models are generally valid only for the conditions under which those measured data were taken. These models also appear that no dependence on the roughness parameter, l-correlation length. In this work, the potential of using the polarimetric SAR data over surface scatterers in order to invert surface parameters is investigated. The model-based and image-based inversion schemes are investigated and compared; the former is doing retrieval from a dynamic learning neural network trained with the Advanced Integral Equation Model, while the latter is schemed from a decomposition of coherency matrix. In model based approach, only the surface scattering term of total return is used in order to remove the vegetation effects. The image based approach accounts for non-zero cross-polarized, backscattering as well as depolarization by three polarimetric parameters, namely the scattering entropy(H), the scattering anisotropy(A), and the alpha angle(alpha). The features of these two schemes are discussed in terms of numerical aspects and physical implications of the surface parameters being inverted by using experimental E-SAR L-band data . We also show the performances of inversion and discuss the advantages and drawbacks of both schemes.

Several methods have been investigated which use fully polarimetric synthetic aperture radar (POLSAR) data to measure directional ocean wave slope spectra. Independent techniques have been developed to measure wave slopes in the orthogonal SAR (azimuth/range) directions. In this paper, wave spectra measured using two polarimetric methods are investigated. Spectra developed using: a) intensity-based polarimetric SAR methods and b) a more recent orientation/alpha angle algorithm are compared. NASA/JPL/AIRSAR L-band data from two-sites in California coastal waters have been used.

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.

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 /spl lscr//sub 1/ and /spl lscr//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 /spl lscr//sub p/ norms. The results support the conclusion that the pixel model is more important than the clustering mechanism.

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/spl deg/(/spl 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 /spl 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.

Polarimetric SAR image classification remains an important research area. Various methods continue to be developed for specific applications. Instead of focusing upon one specific problem, we have developed nonlinear dimensionality reduction techniques that extract information from inherently high dimensional datasets. We present computationally tractable, suboptimal extensions of the full nonlinear dimensionality reduction techniques. We apply these suboptimal techniques to polarimetric SAR image classification. Comparisons will be made between optimal and suboptimal techniques, and, as a reference, to standard statistical Wishart classifiers.

A technique is presented for multiscale texture analysis and segmentation of polarimetric SAR images. Textural features are extracted using a multiscale wavelet decomposition based on a wavelet frame. The feature vector is composed of local variance estimates of the smooth image and of the wavelet coefficients. The decomposition is performed at two scales and using images derived by polarimetric power synthesis at a set of polarization configurations. This set is chosen based on a priori-knowledge of the texturally optimal polarization states. Alternatively a complete and nonredundant representation of the full polarimetric information consisting of nine backscatter intensities is used. Feature reduction is achieved by an approximate solution of the Multiple Discriminant Analysis (MDA) transform. A set of controlled experiments, based on Monte Carlo simulations, is set up to assess the performance of the technique with respect to texture segmentation problems. One case is reported concerning the simulation of a fragmented forest, where two vegetation classes with different structural characteristics are mixed. Finally, as an example of the application of the technique to real SAR data, texture segmentation of a high resolution image acquired by the DLR E-SAR sensor at L-band is illustrated.

The fuzzy c-medians clustering (FCMED) clustering algorithm is known to be a robust l/sub 1/ fuzzy clustering algorithm that works well even in the presence of outliers or remote small clusters. The well-known fuzzy c-means (FCM) clustering algorithm often performs poorly in this environment. Unfortunately, the FCMED has a high space-complexity, which makes its application impractical for large images. A new fast fuzzy c-medians clustering (FFCMED) algorithm is presented, which may be applied to any clustering problem, but is demonstrated here by classifying a POLSAR image. The FFCMED provides a robust clustering tool that works well for large images and data sets. A relative speed up over the FCMED of at least 3-to-1 was observed for the image illustrated in this paper.

An experiment using a polarimetric scatterometer (POLSCAT) has been conducted by JPL for ocean surface wind measurement. It shows that sigma0values for HH, VV, HV, and VH have the property of even symmetry with respect to the upwind direction, and correlation coefficients between co- and cross-polarizations have the odd symmetry property. In this paper, the symmetry properties will be further examined using polarimetric analysis to investigate the depolarization effect, the scattering mechanism, and the polarization orientation angle. Theoretical results based on a two scale model are used to verify the derived experiment results. The newly derived symmetry property has the potential to solve the 180deg ambiguity in wind direction, and to enhance the accuracy of wind vector measurements

Surfactant slicks are widely dispersed throughout the oceans. Current driven features, such as spiral eddies, can be made visible by associated slick patterns. In this study a combined algorithm using the Cloude-Pottier decomposition and the Wishart classifier is presented to produce accurate maps of slick patterns. The study then uses the classified slick patterns to detect spiral eddies. The competing background wave-field is eliminated. Satellite SAR instruments performing wave spectral measurements, or operating as wind scatterometers, regard the slicks as a measurement error-term. The maps produced by the algorithm facilitate the flagging of slick contaminated pixels within the image

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.

Analysis of polarimetric SAR data often proceeds after first identifying important degrees of freedom in the data, thereby reducing the dimensionality of the problem. Various derived parameters (e.g. entropy, polarization fraction) and/or descriptions of polarimetric data (e.g. scattering matrix, covariance matrix, Stokes Parameters) are employed to effectively reduce the dimensionality of the data while at the same time preserving the relevant polarimetric information. The classification presented in this work involves two steps: first determine the reduced set of variables, from the data, that "optimally" represents the polarimetric information. Second, employ these newfound variables to segment (and classify) the polarimetric SAR image. Finally, the classification results are compared with standard statistical maximum likelihood techniques.

In this overview, reasons are provided on why we do need to place multi-modal, multi-band single and multiple pass POLinSAR monitoring platforms into air and space. The questions "on what POLinSAR monitoring can provide that POL-SAR and IN-SAR by themselves cannot accomplish" is assessed; whereupon facts and justifications on placing POL-IN-BISAR satellite clusters into space are presented. Reasons for this technology becoming a basic requirement for current, near-future and much more so for future all day & night year-round monitoring of the terrestrial covers are analyzed in view of the un-abating and uncontrollable terrestrial population explosion, which has, does and for ever will result in unavoidable conflicts deteriorating unfortunately at times into terrorism. The pertinent questions on how to reduce the exorbitant cost for initiating this "home-globe security protection" technology are therefore also broached, and the expected benefits are laid out. The pertinent National and International airborne and space borne multi-modal, multi-band SAR remote sensing and security conflict surveillance support agencies are herewith invited for co-sponsoring our proposal, which is timely and fleets of orbiting multi-band POLinSAR platforms are urgently required to be placed into space.

In this paper, we propose a new speckle filter for polarimetric SAR that preserves pixels' scattering properties. The basic principle is to select pixels of the same scattering characteristics to be included in the average. To achieve this, the algorithm first applies the Freeman and Durden decomposition to separate pixels into three scattering categories: surface, double bounce and volume, and then unsupervised classification is applied. Speckle filtering is performed using the classification map masks. Pixels centered in a 9/spl times/9 window are filtered by including only pixels in the same and two neighboring classes from the same scattering category. This algorithm is extended to the coherence estimation of polarimetric interferometry SAR data. The effectiveness of this new algorithm is demonstrated with JPL AIRSAR and E-SAR data.

We review estimation algorithms and applications of polarization orientation angle shifts induced by terrain slopes. We develop a unified analysis of estimation algorithms based on circular polarization covariance matrix. The effect of radar frequency, scattering media, and polarimetric calibration will also be discussed. Applications to DEM generation, polarimetric SAR data compensation and ocean surface feature characterization will be mentioned. SIR-C, and JPL AIRSAR L-band and P-band polarimetric SAR images are used for demonstration.

The objective of this paper is to investigate the effect of a two-scale surface roughness description on the polarization coherence in circular polarization (i.e. P/sub RRLL/). Under the assumption of sufficiently smooth surfaces, the small-slope approximation (SSA) model is employed to derive a first order expression for P/sub RRLL/. In the paper the sensitivity of P/sub RRLL/ to azimuth slopes is presented.

New methods have been investigated which use fully-polarimetric synthetic aperture radar (SAR) image data to measure ocean wave slope spectra. Independent techniques have been developed to measure wave slopes in the orthogonal SAR (azimuth/range) directions. These measurements must still contend with motion-induced nonlinearities in the SAR processing. The methods are, however, physically based, robust, and utilize a parametrically simpler modulation transfer function. NASA/JPL/AIRSAR L-band data from California coastal waters has been used. Wave spectra measured using the new methods are compared with spectra developed using both conventional SAR intensity-based methods and NDBC buoys.

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 Jet 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.

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.

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/spl times/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.

Fully polarimetric SAR data analysis has found wide application for terrain classification, land-use, soil moisture and ground cover classification. New methods and algorithms continue to be developed and tested. One method that has gained great popularity is the Cloude-Pottier eigenvalue/eigenvector decomposition of coherency matrices (Cloude and Pottier 1996). The eigenvalue spectrum uniquely describes the scattering entropy, anisotropy and span (total power). The eigenvectors are employed in the calculation of averaged quantities. Typically though the eigenvector components have not been analyzed in much detail. Here we present a general analysis of the eigenvectors based on an earlier observation (Cloude 2001) that the eigenvectors may be parameterized by a set of rotations.

Radar Polarimetry, Radar Interferometry and Polarimetric SAR Interferometry represent the current culmination in 'Microwave Remote Sensing' technology, but we still need to progress very considerably in order to reach the limits of physical realizability. Whereas with radar polarimetry the textural fine-structure, target orientation, symmetries and material constituents can be recovered with considerable improvement above that of standard 'amplitude-only' radar; by implementing 'radar interferometry' the spatial (in depth) structure can be explored. With Polarimetric Interferometric Synthetic Aperture Radar (POL-IN-SAR) imaging, it is possible to recover such co-registered textural and spatial information from POL-IN-SAR digital image data sets simultaneously, including the extraction of Digital Elevation Maps (DEM) from either Polarimetric (scattering matrix) or Interferometric (dual antenna) SAR systems. Simultaneous Polarimetric-plus-Interferometric SAR Imaging offers the additional benefit of obtaining co-registered textural-plus-spatial three-dimensional POL-IN-DEM information, which when applied to Repeat-Pass Image-Overlay Interferometry provides differential background validation and environmental stress-change information with highly improved accuracy. Then, by either designing multiple dual polarization antenna POL-IN-SAR systems or by applying advanced POL-IN-SAR image compression techniques, will result in 'POL-arimetric TOMO-graphic' (Multi-Interferometric) SAR or POL-TOMO-SAR imaging.

In this paper is introduced an approach to the classification and interpretation of SAR data using the complementary polarimetric and interferometric information. An unsupervised polarimetric segmentation is applied to one of the separate interferometric dataset. The use of pertinent polarimetric indicators permits to give an interpretation of each resulting cluster scattering mechanism and to classify the observed scene into three canonical scattering types. The interpretation and the segmentation of an optimized interferometric coherency spectrum is applied, for each type of scattering, to initialize an unsupervised statistical interferometric classification procedure.

An analysis technique is presented for quantifying statistically and under a unified framework non-stationarity in polarimetric SAR imagery. The unified framework is provided by a multi-resolution analysis (MRA) based on a particular wavelet frame that works like a differential operator. The wavelet MRA provides local estimates of a statistics called structure function, that in turn can characterize two types of non-stationary behavior: smoothed singularities (e.g. edges, point targets); self-similar processes with stationary increments (e.g. fractional Brownian motion). In the polarimetric case we are interested in the combined dependencies on scale and polarization state. To the purpose an extension of the wavelet MRA is introduced for deriving wavelet representations of an intensity image synthesized at any polarization state (pol-MRA). A novel formalism called the polarimetric structure signature condenses in graphical form the properties of points of discontinuity with respect to scale and polarization transformations. A test case illustrates the application of the pol-MRA technique to the analysis of weak but polarimetrically diverse linear features embedded in clutter.

Fully-polarimetric SAR images from two scenes from the west coast are analyzed to study the modulation of polarimetric coherence by ocean features. Co-polarized and circularly polarized coherences are calculated for ocean surface waves, a weather front, a coastal plume and a ship wake. The study concludes that changes in short scale surface roughness did not modulate the co-polarized coherence. But changes in the dielectric constant did affect this coherence.

Polarimetric SAR interferometry has generated much interest for forest applications. In this paper, we study problems associated with interferometric coherence estimation and propose two algorithms to improve the accuracy in the coherence estimation based on speckle filtering of the 6 /spl times/ 6 polarimetric interferometry matrix.

Studies have shown that polarimetric SAR may be used to measure terrain slopes and surface roughness. These measurement concepts have been extended here to study slope distributions generated by surface manifestations of internal waves, as well as, by convergent current fronts. Measurements/modeling of these ocean features are generally done using backscatter intensity modulations. In this work, internal wave induced changes in the polarimetric orientation angle are measured. These changes are then used to estimate those perturbations in the ambient wave slope distribution and coherence that are directly caused by the internal wave.

In recent studies, D. L. Schuler et al. (2000) applied polarimetric imaging radar-derived orientation angles to measure topography, and J. S. Lee et al. (2000) 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. The present authors 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.

In this paper, the circular polarization coherence, /spl rho//sub 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 |/spl rho//sub RRLL/| with increasing surface roughness ks over a range 0 /spl les/ ks /spl les/ 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 |/spl rho//sub RRLL/| depends on the surface roughness and on the local incidence angle. The dependence of |/spl rho//sub PRRLL/| on the local incidence angle is supported by theory and experimental results. For these same scattering cases, however, |/spl rho//sub PRRLL/| is independent of the surface dielectric constant. Estimation of the functional dependency of |/spl rho//sub PRRLL/| versus ks, for a mid-range incidence angle, has been carried out using roughness estimates derived from an empirical model.

Introduces a new classification scheme for dual frequency polarimetric SAR data sets. A (6/spl times/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/spl times/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.

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 are investigated for application to crop and tree age classification. Based on the complex Wishart distribution for the covariance matrix, maximum likelihood classifiers for all polarization combinations are used to assess quantitative classification accuracy. Thus, this allows optimally selecting the frequency and the combination of polarizations for various applications.

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. The authors 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. They 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 Huynelt'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.

Using simulations of radar cross section (RCS) based on wave-current interaction calculations, the authors 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 (1D) 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. The authors 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 they provide an overview of a number of additional submesoscale features associated with the GS that were present in the image of the GS boundary.

Bright linear features have been observed in radar imagery taken near the Gulf Stream (GS) boundary on two separate occasions. In each case, these have been observed directly over strong current convergences. Progress has been made in understanding the origin of these signatures through simulations that incorporate environmental forcing from the winds and currents. These simulations significantly underestimate the backscatter unless wave-breaking (WB) effects are included at least approximately. Using a new, quasistatistical procedure that generalizes and quantifies earlier procedures for including WB effects, the authors have been able to successfully simulate the magnitude and behavior of these signatures. The approach combines the statistically based, composite model of radar backscatter with a deterministic feature model that relates backscatter from breaking waves to a particular geometrical model of a spilling breaker. This is accomplished using localized criteria, defined by local wave crest acceleration, to determine the probability of breaking, and by extending the feature model so that its unknown parameters may be evaluated directly from wave-current interaction calculations. The new approach provides an estimate of the critical crest acceleration of a potentially breaking wave, as a function of wind speed, that agrees with independent measurements.

The authors 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, S.R. Cloude et al. (1997), and the maximum likelihood classifier based on the complex Wishart distribution for the polarimetric covariance matrix, J.S. Lee et al. (1994). The authors 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. The authors 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.

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.

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.

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.

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.

The authors 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 favourable 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, the authors employ this measurement of the surface currents and wave field near a velocity front to help refine and bound results of their modeling of calculated radar images of the front. The results of this paper are compared with available ground truth.>

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.>

Polarimetric and interferometric SAR data are frequently multilook processed for speckle reduction and data compression. The statistical characteristics of multilook data are quite different from those of single-look data. The authors investigate the statistics of their intensity and phase. Probability density function (PDF's) of the multilook phase difference, magnitude of complex 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.>

Polarimetric signatures and related polarimetric properties of microwave ocean backscatter are analyzed for both the ambient ocean and for ocean features such as those associated with the Gulf Stream. Interpretation of the polarimetric signatures for the ocean surface is accomplished using a tilted-Bragg theoretical model. This model is used to calculate the EM fields, to second order, which is necessary to compute the full Stokes matrix and, ultimately, the polarimetric signature. The polarimetric studies lead to a technique for potentially improving the visibility of all azimuthally traveling waves in real-aperture radar (RAR) images and very long waves in synthetic-aperture radar (SAR) images. This technique utilizes linear polarization signatures to maximize the instrument sensitivity to azimuthally traveling waves. Wave tilts create a modulation of the cell polarization orientation which, in turn, modulates the backscatter. Critical to the success of this technique is that the ocean polarimetric signatures be sharply peaked (i.e., returns be highly polarized). The polarimetric contribution to the overall modulation transfer function is evaluated.>

A generalized principal components transform (PCT) that maximizes the signal-to-noise ratio (SNR) and that tailors to the multiplicative speckle noise characteristics of polarimetric SAR images is developed. An implementation procedure that 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 capabilities of this principal components transformation in information compression and speckle reduction makes automated image segmentation and better human interpretation possible.>

An algorithm to take advantage of this polarization diversity to suppress the speckle effect with much less resolution broadening than using spatial filtering is discussed. The coupling between polarization channels is minimized by using local intensity ratios. The degree of speckle reduction is similar to two-look or three-look processing. The same algorithm can also be used to process multifrequency polarimetric SAR. For three-frequency aircraft SAR data speckle reduction equivalent to six-look processing can be achieved. Further speckle reduction is possible by applying speckle filters in the spatial domain. In addition, a vector speckle filter which operates simultaneously in the polarization and spatial domains is tested. Experimental results with simulated polarimetric SAR as well as one-look and multilook parametric SAR data demonstrate the effectiveness of these speckle reductions, with minimum resolution broadening and coupling between polarimetric and frequency channels. Comparisons with other algorithms are also made.>

Two situations in which the image gray-level histogram cannot be used for threshold determination are: (1) the situation in which the background noise by itself has a multimodal distribution; and (2) the situation in which the object is so small that its contribution to the histogram is overwhelmed by the noise portion even if the noise distribution is unimodal. To alleviate these two undesirable conditions, local average, the central limit theorem, and a statistical theory for truncated data analysis are used to: (1) make the noise part of the histogram appear unimodal; and (2) cut off a large portion of the background so that the object portion in the histogram becomes more prominent. The gray-level distributions for the background and the object are then estimated and used to find an optimum threshold.>

Speckles appearing in synthetic aperture radar (SAR) images are generated by the coherent processing of radar signals. Basically the speckles have the nature of a multiplicative noise. A simple and effective method of smoothing speckle-corrupted images by a digital computer is discussed, based on the recently developed sigma filter. The sigma filter is motivated by the sigma probability of a Gaussian distribution. The pixel to be processed is replaced by an advantage of those neighboring pixels having their gray level within two noise standard deviations from that of the concerned pixel. Consequently the speckles are suppressed without blurring edges and fine detail. Several Seasat SAR images are used for illustration, and comparisons are made with several noise-smoothing algorithms. Extensions of this algorithm to contrast enhancement and signal-dependent noise filtering are also presented. This algorithm is computationally efficient, and has the potential to achieve real or near real-time processing.

Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays are developed based on their local mean and variance. These algorithms are nonrecursive and do not require the use of any kind of transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in real-time digital image processing applications and where a parallel processor can be used. For both the additive and multiplicative cases, the a priori mean and variance of each pixel is derived from its local mean and variance. Then, the minimum mean-square error estimator in its simplest form is applied to obtain the noise filtering algorithms. For multiplicative noise a statistical optimal linear approximation is made. Experimental results show that such an assumption yields a very effective filtering algorithm. Examples on images containing 256 × 256 pixels are given. Results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.