Spatial Invariant Hash Based on Self-Attention Mechanism for Remote Sensing Ship Image Retrieval

Fuwei Huang, Yaxiong Chen, Kai Yan, Yin Ye, Xuehu Liu, Shengwu Xiong

Abstract: In the task of remote sensing ship image retrieval (SIHS), due to significant angle changes and multiscale characteristics of ships in the image, it is difficult to extract advanced features, and the efficiency of feature descriptors is low. Therefore, this letter proposes a spatial invariant hash based on self-attention mechanism for remote SIHS. The algorithm consists of two core modules: first, a module based on spatial invariance is designed, which uses deep convolutional neural network to extract continuous real-valued descriptors, introduces the spatial transformation attention mechanism, and enhances the adaptation ability and learning efficiency of the model to the spatial invariance features through self-learning affine transformation and attention calculation, and second, a module based on self-attention hashing is proposed, which improves the efficiency of image representation by multiscale image embedding, optimizes the attention regularization in the visual encoder, and effectively solves the problem of quantization loss in hash mapping. The experimental results show that the retrieval performance of SIHS algorithm on the GGWS, DSCR, FGSC-23 and FGSCR-42 datasets is superior to the existing methods based on deep features.

External IDs:dblp:journals/lgrs/HuangCYYLX25