Thomas L. Ainsworth

Unsupervised classification using polarimetric decomposition and the complex Wishart classifier

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 Pottiers 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.

Exploiting manifold geometry in hyperspectral imagery

A new algorithm for exploiting the nonlinear structure of hyperspectral imagery is developed and compared against the de facto standard of linear mixing. This new approach seeks a manifold coordinate system that preserves geodesic distances in the high-dimensional hyperspectral data space. Algorithms for deriving manifold coordinates, such as isometric mapping (ISOMAP), have been developed for other applications. ISOMAP guarantees a globally optimal solution, but is computationally practical only for small datasets because of computational and memory requirements. Here, we develop a hybrid technique to circumvent ISOMAPs computational cost. We divide the scene into a set of smaller tiles. The manifolds derived from the individual tiles are then aligned and stitched together to recomplete the scene. Several alignment methods are discussed. This hybrid approach exploits the fact that ISOMAP guarantees a globally optimal solution for each tile and the presumed similarity of the manifold structures derived from different tiles. Using land-cover classification of hyperspectral imagery in the Virginia Coast Reserve as a test case, we show that the new manifold representation provides better separation of spectrally similar classes than one of the standard linear mixing models. Additionally, we demonstrate that this technique provides a natural data compression scheme, which dramatically reduces the number of components needed to model hyperspectral data when compared with traditional methods such as the minimum noise fraction transform.

Improved Sigma Filter for Speckle Filtering of SAR Imagery

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.

A new technique for noise filtering of SAR interferometric phase images

This paper addresses the noise filtering problem for SAR interferogram phase images. The phase noise is characterized by an additive noise model, and a filtering algorithm based on this noise model was developed by filtering noise along fringes. In addition, this filter adaptively adjusts the amount of filtering according to the coherence. The effectiveness of this filter is demonstrated using SIR-C/X-SAR multi-pass generated interferograms.

On the estimation of radar polarization orientation shifts induced by terrain slopes

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.

The Effect of Orientation Angle Compensation on Coherency Matrix and Polarimetric Target Decompositions

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.

Unsupervised classification of polarimetric synthetic aperture Radar images using fuzzy clustering and EM clustering

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.

Comparison of Compact Polarimetric Synthetic Aperture Radar Modes

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.

Unsupervised classification using polarimetric decomposition and complex Wishart classifier

The authors propose a new method for unsupervised classification of terrain types and man-made objects using polarimetric SAR data. This technique is a combination of the unsupervised classification based on the polarimetric target decomposition (Cloude and Pottier, 1997) and the maximum likelihood classifier based on the complex Wishart distribution (Lee et al., 1994). The advantage of this approach is that clusters may be identified by the scattering mechanisms from the target decomposition. The effectiveness of this algorithm is demonstrated using JPL/AIRSAR and SIR-C polarimetric SAR images.

Evaluation and Bias Removal of Multilook Effect on Entropy/Alpha/Anisotropy in Polarimetric SAR Decomposition

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.