高光谱成像技术具有光谱连续、图谱合一，能够以较高的光谱诊断能力对地物目标进行精细化解译，可以大幅增强地物信息的提取能力。充分利用高光谱遥感图像丰富的空间、谱信息，进行观测目标地物的精细化解译，成为近年来遥感领域的研究热点和前沿领域，并在多个相关领域具有巨大的应用价值和广阔的发展前景。本文结合高光谱图像成像特点，对基于稀疏表示理论的高光谱图像处理与分析方法进行综述，概括了高光谱图像处理与分析主要研究，并对各个研究领域与方向进行分析和评价，最后对各研究领域发展提出建议和展望。

Hyperspectral images, which span the visible to infrared spectrum with hundreds of contiguous and narrow spectral bands, are advantageous because of their subtle discriminative spectral characteristics. Owing to the fine spectral differences between various materials of interest, hyperspectral images support improved interpretation capabilities, and they perform important functions in various fields, such as the military, precision agriculture, and mineralogy. The sparsity of signals is a powerful and promising statistical signal modeling tool for hyperspectral image processing and analysis because signals can be compactly represented by only a few coefficients that carry the most important information in a certain basis or dictionary.
A full diagram of Sparse Representation (SR) consists of sparse coding (regression) and dictionary learning. A brief review about this basic theory from the perspective of hyperspectral remote sensing is presented in the second section. With various forms of complex degradation and the demand for resolution enhancement considered, basic theories as well as recent studies in the area of hyperspectral image processing, including denoising, unmixing, super-resolution, and spectral-spatial image fusion in an SR manner, are presented in the third section. Dimensional reduction, classification, target detection, and anomaly detection, which are aimed at mining subtle diagnostic information on hyperspectral images in an SR manner, are also reviewed in the hyperspectral image analysis section. This review is followed by some suggestions and remarks on SR-based hyperspectral image processing and analysis. Processing literature considers denoising as the most fundamental task, single image super-resolution as problematic, and hyperspectral unmixing and spectral-spatial fusion as necessary subjects. In the hyperspectral analysis area, classification and detection are the most important tasks, and feature extraction/dimensional reduction works as a meaningful pre-processing step. The research on SR-based hyperspectral processing and analysis has recently attained some achievements. However, the obstacles mentioned in this paper still require further study.
This review also presents several important remarks and outlines the future directions in this field. First, despite the increasing research on hyperspectral signal recovery and reconstruction, single image super-resolution and multi-resolution image fusion remain a problem area, mainly because of the limited availability of prior information within the remote sensing data and because of unstable sparse inverse optimization. Thus, the method for qualified over-completed dictionary design for specific hyperspectral image processing and delicate inverse optimization is an important study area. Second, the most important issue for hyperspectral image information extraction comes in the form of a discriminative dictionary. Most research ideas on hyperspectral target detection involve multiple sparse representation classification-based model simplification, whereas the anomaly detection method relies on the quality of the local ambient dictionary. Thus, the detection area should be studied further. In addition, reasonable approximated sparse coding combined with specific dictionary learning and some unique characteristics of hyperspectral images, such as manifold structure, multi-modality, and low rank property, should be regarded as useful tools for future research.