Classification SAR targets with support vector machine

Lanying Cao

doi:10.1117/12.696427

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27 February 2007 Classification SAR targets with support vector machine

Lanying Cao

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Proceedings Volume 6497, Image Processing: Algorithms and Systems V; 64970O (2007) https://doi.org/10.1117/12.696427
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

With the development of Synthetic Aperture Radar (SAR) technology, automatic target recognition (ATR) is becoming increasingly important. In this paper, we proposed a 3-class target classification system in SAR images. The system is based on invariant wavelet moments and support vector machine (SVM) algorithm. It is a two-stage approach. The first stage is to extract and select a small set of wavelet invariant moment features to indicate target images. The wavelet invariant moments take both advantages of the wavelet inherent property of multi-resolution analysis and moment invariants quality of invariant to translation, scaling changes and rotation. The second stage is classification of targets with SVM algorithm. SVM is based on the principle of structural risk minimization (SRM), which has been shown better than the principle of empirical risk minimization (ERM) which is used by many conventional networks. To test the performance and efficiency of the proposed method, we performed experiments on invariant wavelet moments, different kernel functions, 2-class identification, and 3-class identification. Test results show that wavelet invariant moments indicate the target effectively; linear kernel function achieves better results than other kernel functions, and SVM classification approach performs better than conventional nearest distance approach.

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Lanying Cao "Classification SAR targets with support vector machine", Proc. SPIE 6497, Image Processing: Algorithms and Systems V, 64970O (27 February 2007); https://doi.org/10.1117/12.696427

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