Abstract
ABSTRACTAzimuth sensitivity is a significant characteristic of synthetic aperture radar (SAR) images. Most of the previous SAR target recognition algorithms try to cope with the property by pose estimation or training classifiers which are not sensitive to azimuth. Actually, the azimuth sensitivity can provide discriminative information for target recognition as a supplement to the original spatial image (SI). This letter describes the azimuth sensitivity by the azimuth sensitivity image (ASI) which is constructed by comparing the sub-aperture images of the SI. Then the SI and ASI are classified by the sparse representation-based classification (SRC), respectively. Afterwards, a score-level fusion is employed to combine the two results for robust target recognition. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset and the performance is compared with several state-of-the-art methods. The experimental results show that the ASI can complement the SI for effective and robust target recognition.
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