A Deep Cross-Modal Hashing Technique for Large-Scale SAR and VHR Image Retrieval
Yuxi Sun, Shanshan Feng, Yunming Ye, Xutao Li, Jian She Kang · 2021
Cross-modal hashing is an important technology for large-scale very high resolution (VHR) and synthetic-aperture radar (SAR) image retrieval. Current cross-modal hashing methods fail to effectively preserve the intra-class similarities and the inter-class discriminations between VHR and SAR images when learning common semantic representation of these cross-modal images. This is because these methods use derived signals to implicitly guide hashing learning, which leads to low discrimination of generated hash codes. To address the drawback, this paper proposes an explicit semantic preserving-based deep hashing method, which can fully learn the intra-class and inter-class semantic structure. Specifically, we design a novel objective function to explicitly preserve the intra-class and inter-class semantic structure directly with class labels. Extensive experiments on a VHR-SAR dataset demonstrate that our method outperforms various state-of-the-art cross-modal hashing methods.