Adaptive Weighting Based on Subimage Sparse Model for SAR Occluded Target Recognition

Abstract

Partially occluded target recognition is a problem that is rarely studied but must be faced in the field of synthetic aperture radar target recognition. The difficulty of this problem is finding out the location of the occluded area and eliminating the effects of occlusion. In this paper, an adaptive weighting method based on subimage sparse model is proposed to recognize the partially occluded targets. This method divides the test image (query) and the dictionary into subimages and subdictionaries by the same gridding method. Then, the sparse reconstruction error of each subimage is calculated to design weighting coefficients for the subimage. By multiplying the subimages and subdictionaries by the corresponding weighting coefficients, the weighted query and the weighted dictionary are obtained. Finally, the sparse representation recognition method is utilized to classify the weighted query. The core idea of the proposed method is that the sparse reconstruction error of the image is defined as the sum of the sparse reconstruction error of all the image pixels, and the difference of the sparse reconstruction error of the subimage between unoccluded condition and occluded condition is obvious. So the sparse reconstruction error of the subimage can be easily extracted and used as an indicator for determining the occluded area. By setting small weighting coefficients in the occluded area, the influence of occlusion is reduced and the recognition performance of partially occluded target is improved. Experimental results demonstrate that the proposed method leads to better recognition result in comparison to some state-of-the-art methods under the occlusion condition.