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Quality improvement of hyperspectral remote sensing images: A technical overview

In hyperspectral remote sensing imagery, the sensor, atmosphere, topography and other factors often bring about some degradations, such as noises, blurring, aliasing, clouding, shadowing, etc. Compensating for these degradations through quality improvement is a key preprocessing step in the exploitation of hyperspectral imagery. In this paper, a comprehensive analysis of the quality improvement techniques for hyperspectral images is presented. In order to embody the differences with those used for other types of images, the methods are classified according to their special processing strategies for hyperspectral images. Except for the description of the theory and methods, some experiments on hyperspectral images, including denoisng, deblurring, inpainting, destriping are illustrated. Some potential methods about this interesting topic are also discussed.