Synthetic Aperture Radar Image Recognition Using Contour Features

Abstract

Synthetic aperture radar (SAR) images play an important role in nowadays target recognition. However, due to the intrinsic impact of speckle noise in SAR images, many details of the targets are confused, thus causing difficulty on target recognition. Therefore, this paper introduces a novel image recognition method which performs well on optical images. In our paper, the target is first extracted by combining the multi-resolution Markov random field method with the wavelet transform. Such a process is able to reduce the speckle noise from different resolutions. Then, morphological filtering is utilized to further reduce the remaining noise as well as smooth the boundary. With the smoothed target, we adopt the state-ofthe- art shape representation method called bag of contour fragments for target classification, which encodes the target into different contours. Then, by using support vector machine (SVM), the contours of each target can be well classified and the target recognition can be achieved. Finally, the experimental results validate the superior performance of our method on the recognition accuracy.