Hyperspectral Image Classification Based on Structured Dictionary Learning

In order to improve the classification accuracy of hyperspectral images, a new structured dictionary-based method for hyperspectral image classification was proposed. This method incorporates both spatial and spectral characteristics of hyperspectral images to obtain a dictionary of each pixel, the pixels in an identical pixel group have a common sparsity pattern;image sparsity representation coefficients are calculated in light of the dictionary to gain sparse representation features of hyperspectral images;the classification of hyperspectral images is determined using a linear support vector machine. Experiments on AVIRIS and ROSIS hyperspectral images were carried out. The experimental results show that compared with the common dictionary learning, the classification accuracy is respectively raised by 0.041 1 and 0.046 6, the Kappa coefficient is improved by 0.179 3 and 0.056 3, respectively.