Abstract
In this paper, we present an efficient compression algorithm for hyperspectral images. The last decade has seen a growing interest in the study of dictionary learning and sparse representation, which have been proved to perform well on natural image compression. Because of the special techniques of hyperspectral imaging, hyperspectral images have some distinct properties when compared with natural images that can affect the design of a compression algorithm. We focus on these properties and propose a novel hyperspectral image compression algorithm by using mixed dictionary and fast representation. Experimental results show that our compression algorithm provides a competitive compression performance compared with most existing compression algorithms.
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