Fusion of Sparse Model Based on Randomly Erased Image for SAR Occluded Target Recognition

Abstract

The recognition of partially occluded targets is a difficult problem in the field of synthetic aperture radar (SAR) target recognition. To eliminate the effect of occlusion, the intuitive idea is to determine the exact location and the size of the occluded area. However, this is very difficult, even impossible in practice. In order to avoid this difficulty and to improve the recognition performance for the partially occluded target, a fusion strategy of the sparse representation (SR) model based on randomly erased images is proposed to recognize the partially occluded target. The proposed method randomly erases some areas many times in both the test samples and the training samples. The erased training samples in each erasure are used to sparsely represent the corresponding erased test sample. Finally, all the SR results are fused to recognize the test sample. The proposed method utilizes random erasure to eliminate the possible occluded region. In addition, this method uses the fusion strategy to overcome under-erasing of the occluded region and erroneous erasure of the unoccluded region. The key parameter of the proposed method is the erasure ratio only. Although the erasure is random, the recognition performance of the method is relatively stable. Therefore, the method can eliminate the influence of occlusion without determining the details of occlusion. The experimental results show that the proposed method is significantly better than the state-of-the-art methods in the case of occlusion. Additionally, the recognition performance of the proposed method is similar to some comparison methods in the case of no occlusion.