Target recognition in SAR imagery based on local gradient ratio pattern

Abstract

AbstractDue to weather- and illumination-independent characteristics, synthetic aperture radar (SAR) has become an important tool for target recognition. Using analysis of existing methods, a new feature of SAR imagery and a valid target recognition strategy are proposed. We first extract the gradient ratio pattern for each pixel based on Weber's law and the local gradient ratio pattern histogram (LGRPH) is then computed. Next, multiscale LGRPH is constructed for dimensionality reduction. Finally, the similarity is obtained by utilizing K–L discrepancy to measure the distance of MLGRPH. The proposed method is theoretically proven to be insensitive to speckle noise, and the adaptability to local gradient variation is also discussed. Experimental results show that the proposed approach is robust in regard to local gradient variation and speckle noise.