Consistency Center-Based Deep Cross-Modal Hashing for Multisource Remote Sensing Image Retrieval

Cross-modal hashing aims to retrieve similar images from large-scale Earth observation (EO) data archives, which typically contain multiple satellite sources of remote sensing (RS) images. However, existing cross-modal hashing methods primarily focus on dual-source RS images and often face two main limitations when retrieving multisource RS images. First, these methods exhibit significant redundancy as they require handling all possible dual-source combinations in multisource RS images. Second, they often rely on pairwise or triplet image sources to construct objective functions, which are not significantly effective in reducing the discrepancies among multiple RS image sources. To address these limitations, we propose a novel consistency center-based deep cross-modal hashing method for multisource RS image retrieval called <inline-formula> <tex-math notation="LaTeX">${\texttt {C}^{2}\texttt {Hash}}$ </tex-math></inline-formula>. Our <inline-formula> <tex-math notation="LaTeX">${\texttt {C}^{2}\texttt {Hash}}$ </tex-math></inline-formula> employs a multibranch hashing network to directly encode multisource RS images into unified hash codes, thereby offering higher processing efficiency. Furthermore, <inline-formula> <tex-math notation="LaTeX">${\texttt {C}^{2}\texttt {Hash}}$ </tex-math></inline-formula> introduces consistency centers to construct a novel objective function. The consistency center represents the shared semantic features among similar multisource RS images and is generated by a label hashing network. The objective function encourages similar multisource RS images to approach the same consistency center to align all image sources in a unified Hamming space. Our method can effectively reduce the discrepancies across multiple image sources and generate unified hash codes. To evaluate its effectiveness, we construct a new multisource RS image dataset called MSRSI, comprising five different types of image sources. We conduct comprehensive experiments to demonstrate the superior performance of our method on the MSRSI dataset (<uri>https://github.com/sunyuxi/C2Hash</uri>).