Target detection in SAR images using SIFT

Target detection in synthetic aperture radar (SAR) images which are affected by speckle noise is a challenging task. An algorithm for automatic target detection in SAR images is proposed in this research work. In the first step, moving and stationary target acquisition and recognition (MSTAR) images are segmented and passed through multiple preprocessing stages (histogram equalization, dilation, position normalization). In the next step, feature extraction based on SIFT is performed. The extracted features from testing images are matched with the features extracted from training images. Thus, the classification of the targets is performed. The results obtained and the comparison with existing algorithms, both are sufficient enough to prove that the proposed algorithm is robust and effective.