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A novel hyperspectral image classification method based on learned dictionary is presented in this paper. Firstly, the sampled image pixel and its classification vector are combined as sample pair. Secondly, defined as a sample vector, the sample pair are used for sparse coding and dictionaries learning. Then, the sparse association between sample pairs is established efficiently. Finally, defined as prior knowledge, the sparse association is used to guide the classification of input image. The whole dictionary learning process can be achieved offline, and improve the speed of the algorithm. Several experiments show that the method can get good classification results.

Hyperspectral Remote Sensing Images Classification Method Based on Learned Dictionary