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5 July 1995 Neural network approach for high-resolution target classification
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We proposed an improved version of the SOFM/LVQ classifier currently used in an ATR system for SAR imagery. This classifier was originally designed to construct a few number of templates to represent a set of targets with different orientations. The classifier accepts an input of a target, computes distances of this data with those representative templates, and then classifies this data to the target class with the shortest distance. In this paper, we focus on the issue of how to identify and reject data from targets outside the given data set, such as man- made clutters. To reject clutters, we propose two discrimination functions, distance and entropy measures. With the distance discriminator, we have obtained a very good classification performance when all data are from the given target sets. However, the simple distance measure produces poor classification results when unknown targets such as natural or manmade clutters are present and when each target is represented by a small number of templates. We correct this deficiency by incorporating an entropy measure into the original classifier. With this entropy discriminator, our system rejects a majority of the false alarms while maintaining a high correct classification rate with a relatively few templates for each target. Although, this system was tested on real ISAR data and showed a very good performance, the data was obtained from `turntable' experiment with a fixed depression angle and known target location. One of the future research directions is to test this algorithm with real `field' SAR data and study the robustness of the system.
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Yi-Chuan Lu and Kuo-Chu Chang "Neural network approach for high-resolution target classification", Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995);

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