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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