Abstract: Hyperspectral remote sensing is one of the frontier technologies in the field of remote sensing.It’s a hot topic in hyperspectral information processing to apply sparse coding model to process hyperspectral remote sensing image.To improve the accuracy of hyperspectral image classification,a hyperspectral remote sensing image classification method based on the second-moment spatial-spectral joint contextual sparse coding(SM-CSC) was proposed.First,a dictionary was obtained by training the samples selected from the ground-truth data,then the sparse coefficient of each pixel was calculated based on the learned dictionary.Afterward,the sparse coefficient was inputted to the classifier and the final classification result was obtained.The visible and near-infrared hyperspectral remote sensing image collected by Tiangong-1 in Chaoyang District of Beijing and the KSC hyperspectral image were applied to estimate the performance of the proposed approach.Comparisons with three other classification methods such as support vector machine(SVM),spectral sparse coding(SSC),and first-moment spatial-spectral joint contextual sparse coding(FM-CSC) were made.Experimental results show that the proposed method can yield the best classification performance with the overall accuracy of 95.74% and the Kappa coefficient of 0.9476 on the Tiangong-1 data and with the overall accuracy of 96.84% and the Kappa coefficient of 0.9646 on the KSC data.

Key words: Classification, Hyperspectral remote sensing image, Sparse coding