Automatic Target Detection Method for High-Resolution Synthetic Aperture Radar Images

Abstract

Automatic target detection is of great importance in high-resolution synthetic aperture radar (SAR) images processing. In this paper, we proposed a hybrid HMM-TSVM model to detect targets in SAR images. Our proposed SAR image target detection system is made up of three steps. In this first step, the testing/training SAR images are pre-processed, and image visual features are extracted through 2DPCA, which is an improved version of principal component analysis. In the second step, we use the HMM model to construct the training sequence from training image dataset. Particularly, the relationships between the image in the azimuth and feature vectors are used to generate the feature sequences and training sequences for hidden Markov model. Furthermore, the feature sequences and training sequences are extracted from feature vectors with similar target images in an azimuth. In the third step, targets are detected from testing SAR images by TSVM classifier based on the training sequence by exchanging the labels of pair of different unlabeled samples to solve an objective function. Experimental results demonstrate the effectiveness of the proposed algorithm