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Hyperspectral Imaging and Image Processing

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Closed
Satellite images captured by hyperspectral sensors can cover the visible and infrared wavelengths with more than hundred spectral bands for each pixel. The informative spectral information recorded in hyperspectral images is directly related to the physical nature of the different materials, and thus, can be utilized for a variety of remote sensing applications such as mineral identification, accurate agriculture, environment monitoring, ground cover classification, object or anomaly detection, change detection, and so on. Furthermore, hyperspectral imaging is also a versatile tool for non-remote sensing problems such as food inspection and biomedical analysis. These meaningful and important applications have led to a wide variety of hyperspectral image processing problems, which usually cannot be solved by a straight application of traditional image processing methods. In order to solve these problems, many creative hyperspectral image processing methods have been developed. Some of them turn out to provide new insights for fundamental signal and image processing research. For example, the classification topic in hyperspectral remote sensing has formed a new branch of scene classification, where in the utilization of contextual information or combined spectral-spatial processing, has been found to provide very effective solutions. Moreover, the recent research trend indicates that hyperspectral image processing is embracing frontier image processing concepts very quickly, such as deep learning, sparse representation, and compressive sensing.

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