LOCAL BROWNIAN DESCRIPTOR BASED FEATURE EXTRACTION METHOD FOR HYPERSPECTRAL IMAGE CLASSIFICATION

In this paper, a novel local Brownian descriptor (LBD) based feature extraction method is proposed for hyperspectral image (HSI) classification. Compared with the classical correlation feature that only characterizes the linear relationship among spectral bands, the LBD can measure both linear and nonlinear relationships to provide much richer spatial-spectral information for HSI classification. Specifically, the HSI is firstly mapped into a low-dimensional spectral subspace by utilizing the maximum noise fraction (MNF) method. Based on the subspace, pixels in the square neighborhood of each sample construct a sample block. Then, the LBD as the local correlation feature is calculated for each sample block. Finally, a kernel sparse representation (KSR) classifier is utilized on these feature descriptors, leading to the final classification results. Experiments conducted on a real hyperspectral image demonstrate the outstanding performance of the proposed method over several state-of-the-art methods.