Hyperspectral image (HSI) with high spectral resolution often suffers from low spatial resolution owing to the limitations of imaging sensors. Image fusion is an effective and economical way to enhance the spatial resolution of HSI, which combines HSI with higher spatial resolution multispectral image (MSI) of the same scenario. In the past years, many HSI and MSI fusion algorithms are introduced to obtain high-resolution HSI. However, it lacks a full-scale review for the newly proposed HSI and MSI fusion approaches. To tackle this problem, this work gives a comprehensive review and new guidelines for HSI–MSI fusion. According to the characteristics of HSI–MSI fusion methods, they are categorized as four categories, including pan-sharpening based approaches, matrix factorization based approaches, tensor representation based approaches, and deep convolution neural network based approaches. We make a detailed introduction, discussions, and comparison for the fusion methods in each category. Additionally, the existing challenges and possible future directions for the HSI–MSI fusion are presented.

由于成像传感器的局限性，具有高光谱分辨率的高光谱图像（HSI）通常遭受空间分辨率低的困扰。图像融合是一种增强HSI空间分辨率的有效且经济的方法，该方法将HSI与相同场景的更高空间分辨率多光谱图像（MSI）相结合。近年来，为了获得高分辨率的HSI，引入了许多HSI和MSI融合算法。但是，它缺乏对新提出的HSI和MSI融合方法的全面评估。为了解决这个问题，这项工作为HSI-MSI融合提供了全面的回顾和新的指南。根据HSI-MSI融合方法的特点，将它们分为四类，包括基于泛锐化的方法，基于矩阵分解的方法，基于张量表示的方法，和基于深度卷积神经网络的方法。我们对每种类别的融合方法进行了详细的介绍，讨论和比较。此外，还介绍了HSI-MSI融合的现有挑战和可能的未来方向。