Michael C. Burl

Optimal speckle reduction in polarimetric SAR imagery

Speckle is a major cause of degradation in synthetic aperture radar (SAR) imagery. With the availability of fully polarimetric SAR data, it is possible to use the three complex elements (HH, HV, VV) of the polarimetric scattering matrix to reduce speckle. The optimal method for combining the elements of the scattering matrix to minimize image speckle is derived, and the solution is shown to be a polarimetric whitening filter (PWF). A simulation of spatially correlated, K-distributed, fully polarimetric clutter is then used to compare the PWF with other, suboptimal speckle-reduction methods. Target detection performance of the PWF, span, and single-channel mod HH mod /sup 2/ detectors is compared with that of the optimal polarimetric detector (OPD). A novel, constant-false-alarm-rate (CFAR) detector (the adaptive PWF) is as a simple alternative to the OPD for detecting targets in clutter. This algorithm estimates the polarization covariance of the clutter, uses the covariance to construct the minimum-speckle image, and then tests for the presence of a target. An exact theoretical analysis of the adaptive PWF is presented; the algorithm is shown to have detection performance comparable with that of the OPD. >

Optimal polarimetric processing for enhanced target detection

The results of a study of several polarimetric target detection algorithms are presented. The study concerns the Lincoln Laboratory millimeter-wave SAR sensor, a fully polarimetric, 35 GHz synthetic-aperture radar. Fully polarimetric measurements (HH, HV, VV) are processed into intensity imagery using adaptive and nonadaptive polarimetric whitening filters (PWFs), and the amount of speckle reduction is quantified. Then a two-parameter CFAR (constant false alarm rate) detector is run over the imagery to detect the targets. Nonadaptive PWF processed imagery is shown to provide better detection performance than either adaptive PWF processed imagery or single-polarimetric-channel HH imagery. In addition, nonadaptive PWF processed imagery is shown to be visually clearer than adaptive PWF processed imagery.<<ETX>>

Texture discrimination in synthetic aperture radar imagery

Texture-based features are used to discriminate between man-made objects and natural ground clutter in high resolution synthetic aperture radar (SAR) imagery. Three features are used for discrimination -the fractal dimension, the log standard deviation, and the ranked till ratio. The fractal dimension provides.information about the spatial distribution of the brightest scatterers in a region, while the log standard deviation provides information about the lluctuations in intensity (radar cross-section) across a region. The ranked fill ratio measures the fraction of energy contained in the brightest scatterers in a region. The effectiveness of these features in texture discrimination is demonstrated using high-resolution SAR imagery gathered by the Advanced Detectlon Technology Sensor.

On the performance of polarimetric target detection algorithms

The performance of six polarimetric target detection algorithms is analyzed. The detection performance of the optimal polarimetric detector (OPD), the identity-likelihood-ratio-test (ILRT), the polarimetric whitening filter (PWF), the single-polarimetric-channel detector, the span detector, and the power maximization synthesis (PMS) detector is compared. Results for both probabilistic and deterministic targets in the presence of complex-Gaussian clutter are presented. The results of these studies indicate that the PWF and the ILRT typically achieve near optimal performance. Each remaining detection algorithm typically yields performance that is degraded compared to the performance of the OPD, the PWF, and the ILRT.<<ETX>>

Optimal Speckle Reduction In Pol-SAR Imagery And Its Effect On Target Detection

Speckle is a major cause of degradation in synthetic aperture radar (SAR) imagery. With the availability of fully polarimetric SAR data, it is possible to use the three complex elements (HH, HV, VV) of the polarimetric scattering matrix to reduce speckle. This paper derives the optimal method for combining the elements of the scattering matrix to minimize image speckle; the solution is shown to be a polarimetric whitening filter (PWF). A simulation of spatially correlated, K-distributed, fully polarimetric clutter is then used to compare the PWF with other, suboptimal speckle-reduction methods. Target detection performance of the PWF, span, and single-channel |HH| detectors is compared with the optimal polarimetric detector (OPD). Finally, a new, constant false alarm rate (CFAR) detector (the adaptive PWF) is proposed as a simple alternative to the OPD for detecting targets in clutter. This algorithm estimates the polarization covariance of the clutter, uses this covariance to construct the minimum speckle image, and then tests for the presence of a target. An exact theoretical analysis of the adaptive PWF is presented; the algorithm is shown to have detection performance comparable with that of the OPD.

Optimal polarimetric processing of SAR imagery

The Advanced Detection Technology Program has as one objective the application of fully polarimetric, high-resolution radar data to the detection, discrimination, and classification of stationary targets. In support of this program, the Advanced Detection Technology Sensor (ADTS), a fully polarimetric, 35-GHz SAR with 1 ft by 1 ft resolution was developed. In April of 1989, the ADTS gathered target and clutter data near Stockbridge, NY. Data from this collection is being used to investigate optimal polarimetric processing techniques. This paper summarizes the results of a recent study of an optimal polarimetric method for reducing speckle in SAR imagery.

Multiple image processing to enhance stationary target detection

One of the methods that can be used to enhance stationary target detection performance is to combine radar data from several looks at an area that may contain targets. This paper presents a study of several multilook techniques. The data used in the study were collected using the MIT Lincoln Laboratory 33.6 GHz Synthetic Aperture Radar (SAR) in the spotlight mode; this mode maintains the radar beam on the same area as the aircraft flies by. Consecutive 0.3 m by 0.3 m resolution images were registered to a single coordinate frame, and then combined in various ways. The processing techniques studied included some methods that combine the data prior to detection (such as noncoherent averaging, which reduces speckle), and others- that combine the detections from individual images (such as techniques that require m detections in n images).

Polarimetric segmentation of SAR imagery

This paper considers the problem of clutter segmentation in fully polarimetric, high-resolution, synthetic aperture radar (SAR) imagery. The goal of segmentation is to partition an image into regions of homogeneous terrain types (grass regions, tree regions, roads, etc.). Three approaches to segmentation are examined: (1) the optimal polarimetric classifier, (2) the optimal normalized polarimetric classifier, and (3) the polarimetric whitening filter (PWF) classifier. Segmentation performance results are presented for typical high-resolution, polarimetric SAR data gathered by the Lincoln Laboratory 35-GHz airborne sensor.