Object Classification in SAR Imagery With Deep Cross-Modal Transfer Network

Object classification in synthetic aperture radar (SAR) imagery is a challenging task, due to the low resolution and high-level speckle noise. The electro-optical (EO) images provide complementary information to SAR images, and thus, the knowledge from the EO images can be used to improve the SAR imagery interpretation. This letter proposes a deep cross-modal transfer network for object classification in SAR imagery. Specifically, the knowledge from the teacher model constructed on the EO images is transferred to the student model for mining discriminative fine-grained features of SAR images. Meanwhile, to perform the cross-modality transferring and fine-grained feature mining, a novel loss function is developed. Experimental results on real-world datasets demonstrate the effectiveness and reliability of the proposed method.