Automatic target recognition (ATR) performance of synthetic aperture radar (SAR) is highly dependent on the sensitivity of SAR images to observing angle. Hence, jointly using of multi-view images of the same target is an efficient way to improve ATR accuracy, since multi-view images carry more correlated information than single-view image. Taking into account heterogeneous multi-views with random not uniform observing interval, an ATR approach with joint sparse representation over a locally adaptive dictionary is investigated in this paper. The first step is to establish a locally adaptive dictionary using sparse representation (SR) after training samples dimension reduction process by Independent and Identically Distributed (IID) Gaussian random project matrix. The locally adaptive dictionary is able to alleviate the limitation of target pose by adjusting the use of information in images and between images with the interval changing. Then heterogeneous multi-view test samples are re-presented by selecting atoms from the locally adaptive dictionary using joint sparse representation (JSR). In such way, high recognition accuracy is guaranteed by combination of more target information and adjustment of the inter-correlation information guarantee. Experiments based on the Moving and Stationary Target Acquisition and Recognition database verify the performance of the proposed algorithm.
['Zongjie Cao', 'Liyuan Xu', 'Jilan Feng']
Automatic target recognition with joint sparse representation of heterogeneous multi-view SAR images over a locally adaptive dictionary