An algorithm of classified redundant dictionary of hyperspectral image based on geologic feature

With the wide application of hyperspectral images in various fields, the theory of sparse representation of signals has been getting more attention from researchers. This paper presents a classified redundant dictionary-learning algorithm of hyperspectral image based on geologic feature. Experimental results showed that hyperspectral image signals could be expressed with the greatest fidelity. The algorithm constructs redundant dictionary library through a sparse decomposed image according to the geologic feature based on clustering, which more accurately represents the spectral signal in sparse reconstruction. To apply the classification dictionary library to super-resolution restoration of hyperspectral image, sparse decomposed hyperspectral image is needed to obtain high and low resolutions of a redundant dictionary. Then, low resolution and dictionary of hyperspectral image restoration could be obtained. The algorithm could effectively improve image resolution to ensure the quality of image restoration.