Abstract: As an important component of multimodal remote sensing data, hyperspectral images are capable of capturing the fine spectral profiles of objects. However, due to the limitations of the imaging mechanism, the loss of spatial details leads to degradation of the spatial representation capability of hyperspectral images, which to a certain extent limits the potential for further applications. Data fusion is an effective approach to resolve the contradiction in spatial and spectral domains, where related theories have intensively developed in recent years. This paper provides a comprehensive overview of the advances and prospects in hyperspectral and multispectral remote sensing image super-resolution fusion. Firstly, fusion algorithms are systematically introduced and classified into three categories, namely, detail injection-based, model optimization-based, and deep learning-based methods. The principles, models and representative algorithms of different methods are reviewed, with emphasis on matrix decomposition, tensor representation in model optimization-based methods, and supervised and unsupervised methods in deep learning-based methods. On this basis, successful applications of the techniques in the field of pixel-level classification,target extraction,and on-board fusion are given, finding that the potential of fusion products has not been fully exploited in subsequent applications. Then, prospective directions are discussed from four aspects, including degradation models, data-model-driven fusion, multi-task integration fusion and application-coupled fusion. Finally, the current process and prospects of the future development trend in this field are summarized, pointing out the strengths and weaknesses of various approaches while highlighting the importance of multi-approach association, external data assistance, application-driven fusion, et al.

Key words: multimodal, hyperspectral, multispectral, data fusion