Automatic Association between Sar and Optical Images based on Zero-Shot Learning

Takahiro Toizumi, Kazutoshi Sagi, Yuzo Senda

Abstract: This paper presents a method for automatic association between synthetic aperture radar (SAR) and optical images based on zero-shot learning (ZSL). SAR target recognition is an important task in security and defense areas, however, it is still challenging due to insufficient labeled SAR images. In order to solve this problem, we propose a ZSL based SAR target recognition. The conventional ZSL transfers information of observed labels in order to recognize unseen objects. The proposed ZSL replaces the labels with optical images corresponding to SAR targets. The proposed method employs ZSL in conjunction with dimension reduction to match different types of unseen images in a compact feature space with high accuracies. When principle component analysis (PCA) is used for the dimension reduction, the proposed method achieves 0.70 in accuracy while a conventional one shows 0.57.