An effective collaborative representation algorithm for hyperspectral image classification

In this paper, an effective l₂-norm collaborative representation algorithm based on 3D discrete wavelet transform (3D-DWT) features, called CR-DWT, is proposed for hyperspec-tral image classification. By using the discriminative 3D-DWT features extracted from the original spectral space, a non-parametric and efficient l₂-norm CR method is developed to calculate the representation coefficients. Due to the simplicity of the method, the computational cost has been substantially reduced, thus all the extracted 3D-DWT texture features can be directly utilized to code the test sample, which greatly improves the classification accuracy of the l₂-norm CR mechanism. The extensive experiments on two real hy-perspectral data sets have shown higher performance of the proposed CR-DWT approach over the state-of-the-art methods in the literature, in terms of both the accuracy and classifier complexity.