论文标题
高光谱和多光谱图像融合的最新进展和新指南
Recent Advances and New Guidelines on Hyperspectral and Multispectral Image Fusion
论文作者
论文摘要
高光谱分辨率的高光谱图像（HSI）通常由于成像传感器的局限性而遭受低空间分辨率。图像融合是增强HSI空间分辨率的有效且经济的方法，该方法将HSI与同一情况的较高空间分辨率的多光谱图像（MSI）结合在一起。在过去的几年中，引入了许多HSI和MSI融合算法以获得高分辨率HSI。但是，对于新提出的HSI和MSI融合方法，它缺乏全面评论。为了解决这个问题，这项工作为HSI-MSI融合提供了全面的审查和新指南。根据HSI-MSI融合方法的特征，它们被归类为四个类别，包括基于pan-sharpenting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。我们对每个类别的融合方法进行了详细的介绍，讨论和比较。此外，提出了HSI-MSI融合的现有挑战和未来的指示。
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.
