Deep Supervised Cross-modal Hashing for Ship Image Retrieval

The retrieval of multimodal ship images obtained by remote sensing satellites is an important content of remote sensing data analysis, which is of great significance to improve the ability of marine monitoring. In this paper, We propose a novel cross-modal ship image retrieval method, called Deep Supervised Cross-modal Hashing(DSCMH). It consists of a feature learning part and a hash learning part used for feature extraction and hash code generation separately, both two parts have modality-invariant constraints to keep the cross-modal invariability, and the label information is also brought to supervise the above process. Furthermore, we design a class attention module based on the cross-modal class center to strengthen class discrimination. The experiment results show that the proposed method can effectively improve the cross-modal retrieval accuracy of ship images and is better than several state-of-the-art methods.