Spatial Correlation Constrained Sparse Representation for Hyperspectral Image Classification

A novel classification method of hyperspectral image based on sparse representation is proposed. First, the training data is used to design a structured dictionary, and a classification model of hyperspectral image is built based on sparse representation; Then the spatial correlation and the spatial information of training data are added to improve the accuracy of this model; Finally it is solved by the rapid alternating direction method of multipliers. The experimental results show that the proposed method can improve the classification results, and the results are stable.