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

In the task of remote sensing ship image retrieval, due to the significant Angle changes and multi-scale characteristics of ships in the image, it is difficult to extract advanced features and the efficiency of feature descriptors is low. Therefore, this paper proposes a spatial invariant hash based on self-attention mechanism for remote sensing ship image retrieval (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, and 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; Secondly, a module based on self-attention hashing is proposed, which improves the efficiency of image representation by multi-scale 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 GGWS, DSCR, FGSC-23 and FGSCR-42 datasets is superior to the existing methods based on deep features.