Classification of Hyperspectral Image Based on K-Means and Structured Sparse Coding

The combination of spatial and spectral information of hyperspectral image benefits the improvement of classification accuracy. The structured sparse coding is proposed to reconstruct the pixels of hyperspectral image. The reconstructed pixels characterize the spatial structure. The K-means method is used to form the dictionary, which has stronger representation ability. Finally, the classification is implemented according to the reconstruction residuals. The experiments are conducted on AVIRIS and the results show that the classification accuracy is improved obviously compared with the other state-of-the-art methods.