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.

高光谱图像处理与分析;稀疏表示;高维信号处理;图像质量改善;影响分类