Enhanced SVM versus Several Approaches in SAR Target Recognition

Abstract

This paper presents a comparative study between different automatic target recognition (ATR) approaches in the application of synthetic aperture radar (SAR) target recognition. Four different categories of approaches are investigated and compared. The first is distribution-based where a statistical data model is assumed for the SAR image data. The second category contains one approach that is based upon principal component analysis (PCA). The third category employs different neural network architectures. The last category utilizes support vector machines (SVM). It contains the classical SVM implementation and an enhanced implementation proposed elsewhere by the authors in which the traditional Euclidean kernel is replaced by a new one that is more suitable for the application in question. Experimental results are presented. It is shown that the enhanced SVM approach outperforms all other investigated approaches in both the classification performance and the confuser rejection