Gregory J. Owirka

Effects of polarization and resolution on SAR ATR

Lincoln Laboratory is investigating the detection and classification of stationary ground targets using high resolution, fully polarimetric, synthetic aperture radar (SAR) imagery. A study is summarized in which data collected by the Lincoln Laboratory 33 GHz SAR were used to perform a comprehensive comparison of automatic target recognition (ATR) performance for several polarization/resolution combinations. The Lincoln Laboratory baseline ATR algorithm suite was used, and was optimized for each polarization/resolution case. Both the HH polarization alone and the optimal combination of HH, HV, and VV were evaluated; the resolutions evaluated were 1 ft/spl times/1 ft and 1 m/spl times/1 m. The data set used for this study contained approximately 74 km/sup 2/ of clutter (56 km/sup 2/ of mixed clutter plus 18 km/sup 2/ of highly cultural clutter) and 136 tactical target images (divided equally between tanks and howitzers).

Automatic target recognition using enhanced resolution SAR data

Using advanced technology, a new automatic target recognition (ATR) system has been developed that provides significantly improved target recognition performance compared with ATR systems that use conventional synthetic aperture radar (SAR) image-processing techniques. This significant improvement in target recognition performance is achieved by using a new superresolution image-processing technique that enhances SAR image resolution (and image quality) prior to performing target recognition. A computationally efficient two-level implementation of a template-based classifier is used to perform target recognition. The improvement in target recognition performance achieved using superresolution image processing in this new ATR system is quantified.

Performance of 10- and 20-target MSE classifiers

MIT Lincoln Laboratory is responsible for developing the ATR (automatic target recognition) system for the DARPA-sponsored SAIP program; the baseline ATR system recognizes 10 GOB (ground order of battle) targets; the enhanced version of SAIP requires the ATR system to recognize 20 GOB targets. This paper presents ATR performance results for 10- and 20-target mean square error (MSE) classifiers using high-resolution SAR (synthetic aperture radar) imagery.

Radar target identification using spatial matched filters

Abstract The application of spatial matched filter classifiers to the synthetic aperture radar (SAR) automatic target recognition (ATR) problem is being investigated at MIT Lincoln Laboratory. Initial studies investigating the use of several different spatial matched filter classifiers in the framework of a 2D SAR ATR system are summarized. In particular, a new application is presented of a shift-invariant, spatial frequency domain, 2D pattern-matching classifier to SAR data. Also, the performance of this classifier is compared with three other classifiers: the synthetic discriminant function, the minimum average correlation energy filter, and the quadratic distance correlation classifier.

Template-based SAR ATR performance using different image enhancement techniques

The Lincoln Laboratory baseline ATR system for synthetic aperture radar (SAR) data applies a super-resolution technique known as high-definition vector imaging (HDVI) before the input image is passed through the final target classification subsystem. In previous studies, it has been demonstrated that HDVI improves target recognition performance significantly. Recently, however, several other viable SAR image enhancement techniques have been proposed and discussed in the literature which could be used in place of (or perhaps in conjunction with) the HDVI technique. This paper compares the performance achieved by the Lincoln Laboratory template-based classification subsystem when these alternative image enhancement techniques are used instead of the HDVI technique. In addition, empirical evidence is presented suggesting that target recognition performance could be further improved by fusing the classifier outputs generated by the best image enhancement techniques.

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.

An efficient multi-target SAR ATR algorithm

The MIT Lincoln Laboratory has developed the ATR (automatic target recognition) system for the DARPA-sponsored SAIP program; the baseline ATR system recognizes 10 GOB (ground order of battle) targets; the enhanced version of SAIP requires the ATR system to recognize 20 GOB targets. This paper compares ATR performance results for 10- and 20-target MSE (mean-squared error) classifiers using medium-resolution SAR (synthetic aperture radar) imagery.

ATR performance using enhanced resolution SAR

MIT Lincoln Laboratory has developed a compete, end-to-end, automatic target detection/recognition (ATD/R) system for synthetic aperture radar (SAR) data. A data-adaptive approach has been developed to enhance SAR image resolution based on super-resolution techniques; this approach is called high-definition imaging. This paper quantifies the improvement in ATR performance from enhanced resolution SAR imagery in the Lincoln Laboratory ATD/R system.

Radar target identification using an eigen-image approach

In order to maintain a high probability of correct classification the classifier must provide good separation between target classes and must be robust with respect to target variability. The authors have implemented a new target classifier based upon the eigen-image concept developed by Turk and Pentland (see Journal of Cognitive Neuroscience, vol.3, no.1, 1991) for automatic recognition of human faces. This paper describes their new eigen-image classifier and presents preliminary performance results for a three-class (tank, APC, gun) classifier. Performance results are compared with those of a shift-invariant pattern matching classifier and a quadratic distance correlation classifier. The algorithms are compared by presenting classifier-performance confusion matrices, which indicate the probability of correct and incorrect target classification. The ability of each classifier to reject cultural false alarms (buildings, bridges, etc.) is also quantified.<<ETX>>