SAR classification and confuser and clutter rejection tests on MSTAR ten-class data using Minace filters

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). In our previous work, we used the MSTAR public database benchmark three-class problem and demonstrated better results than all prior work. In this paper, we address classification (including variants) and object and clutter rejection tests on the more challenging MSTAR ten-class public database. The Minace algorithm is shown to generalize well to this larger classification problem. We use several filters per object, but fewer DIFs per object than prior work did. We use our autoMinace algorithm that automates selection of the Minace filter parameter c and selection of the training set images to be included in the filter. No confuser, clutter, or test set data are present in the training or the validation set. 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 also address tests with proper use of SAR pose estimates in MSTAR recognition and the use of multilook SAR data to improve performance.

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