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This study aims at improving fine-grained ship classification performance under the condition that there is no labeled samples available in SAR domain (target domain) by transferring the knowledge from optical remote sensing (ORS) domain (source domain), which has rich labeled samples. The proposed method improves the original deep subdomain adaptation network (DSAN) by designing a dual-branch network (DBN) embedding attention module to extract more discriminative deep transferable features, thereby improving the performance of the subdomain adaptation. Specifically, we utilized a deep base network (ResNet-50) and a shallow base network (ResNet-18) to build the DBN, and embedded the convolutional block attention module after the first and the last convolutional layer of each branch. Extensive experiments demonstrate that the proposed method, which is termed as DSAN++, is feasible and achieves remarkable improvement than the state-of-the-art methods on the task of fine-grained ship classification.

Improving Deep Subdomain Adaptation by Dual-Branch Network Embedding Attention Module for SAR Ship Classification