To improve the classification accuracy of a hyperspectral image

double L2 sparse coding is proposed in this paper. Pre-processing work was conducted on the hyperspectral image. In this process

the spatial and spectral information of the image were integrated adequately. Based on spatial continuity

the L2 sparse coding was introduced to reconstruct each pixel of the hyperspectral image. A pixel was represented by linear combination of all pixels in its neighborhood. This representation integrated spatial and spectral information

which benefited classification. The L2 sparse coding was used to achieve hyperspectral image classification according to construction error. Moreover

a coding coefficient was introduced into classification principles because of its distinguishable information. Experiments were conducted on a publicly available hyperspectral image database called AVIRIS. To validate the effectiveness of the proposed method

the comparison with SVM

KNN

and L1 sparse coding was carried out using both original and reconstructed images. The proposed method outperformed earlier approaches and improved the accuracy of classification of the hyperspectral image effectively

and then 99.44% classification accuracy was obtained. The method proposed in this paper can be effectively applied to the classification of hyperspectral images.