MSTAR object classification and confuser and clutter rejection using Minace filters

Rohit Patnaik; David Casasent

doi:10.1117/12.663127

18 May 2006 MSTAR object classification and confuser and clutter rejection using Minace filters

Rohit Patnaik, David Casasent

Proceedings Volume 6234, Automatic Target Recognition XVI; 62340S (2006) https://doi.org/10.1117/12.663127
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

This paper presents the status of our SAR automatic target recognition (ATR) work on the Moving and Stationary Target Acquisition and Recognition (MSTAR) public database using the minimum noise and correlation energy (MINACE) distortion-invariant filter (DIF). We use a subset of the MSTAR public database for the benchmark three-class problem and we address confuser and clutter rejection. To handle the full 360° range of aspect view in MSTAR data, we use a set of Minace filters for each object; each filter should recognize the object (and its variants) in some angular range. We use fewer DIFs per object than prior work did. The Minace parameter c trades-off distortion-tolerance (recognition) versus discrimination (confuser/clutter rejection) performance. Our filter synthesis algorithm automatically selects the Minace filter parameter c and selects the training set images to be included in the filter, so that the filter can achieve both good recognition and good confuser and clutter rejection performance; this is achieved using a training and validation set. In our new filter synthesis method, no confuser, clutter, or test set data are used. The peak-to-correlation energy (PCE) ratio is used as the correlation plane metric in both filter synthesis and in tests, since it works better than correlation peak height. In tests, we do not assume that the test input's pose is known (as most prior work does), since pose estimation of SAR objects has a large margin of error; we describe our procedure for proper use of pose estimates in MSTAR recognition. The use of circular versus linear correlations is addressed. We also address the use of multi-look SAR data to improve performance.

Citation Download Citation

Rohit Patnaik and David Casasent "MSTAR object classification and confuser and clutter rejection using Minace filters", Proc. SPIE 6234, Automatic Target Recognition XVI, 62340S (18 May 2006); https://doi.org/10.1117/12.663127

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