Transferable SAR Image Classification Crossing Different Satellites Under Open Set Condition
Siyuan Zhao, Zenghui Zhang, Tao Zhang, Weiwei Guo, Ying Luo
Abstract
For synthetic aperture radar (SAR) image classification problem, we need to take into account unlabeled datasets containing unknown classes crossing different satellites. In this letter, a spherical space domain adaptation (DA) network under open set condition is proposed to solve this problem. First, we transform the prior Euclidean feature space into the spherical space to construct a classification network such that features of the same class of SAR images are clustered together and features of different or unknown classes are separated on the hypersphere. Second, a correction module is designed to increase the accuracy of the pseudo-label obtained by the classifier. Then, based on the adversarial learning strategy, we introduce the gradient alignment module to achieve better alignment of the source and target domains. Finally, tests on two SAR benchmark datasets from distinct satellites show that the proposed network outperforms state-of-the-art (SOTA) approaches in terms of classification accuracy.