HRR PROFILES TIME-FREQUENCY NON-NEGATIVE SPARSE CODING FOR SAR TARGET CLASSIFICATION

A new approach to classify synthetic aperture radar (SAR) targets is presented based on high range resolution (HRR) proflles time-frequency matrix non-negative sparse coding (NNSC). Firstly, SAR target images have been converted into HRR proflles. And the non-negative time-frequency matrix for each of the proflles is obtained by using an adaptive Gaussian representation (AGR). Secondly, NNSC is applied to learn target time-frequency basis of the training set. Feature vectors are constructed by projecting each HRR proflle time-frequency matrix to low dimensional time-frequency basis space. Finally, the target classiflcation decision is found with support vector machine and nearest neighbor algorithm respectively. To demonstrate the performance of the proposed approach, experiments are performed with Moving and Stationary Target Acquisition and Recognition (MSTAR) public release SAR database. The experimental results support the efiectiveness of the proposed technique for SAR target classiflcation.