A Band Grouping Based Hyperspectral Imagery Classification Method With Analysis Dictionary Learning

Dictionary learning (DL) has became popular in image classification tasks. Due to the discriminative analysis dictionary learning (DADL) model can supply richer feature representations and discriminability, it gradually received extensive attention. Inspired by this, we propose a band grouping based hyperspectral imagery classification method with analysis dictionary learning framework in this study. First, we segment all spectra into several segments according to the spectral correlation. Second, DADL is utilized to represent the data and obtain the sparse representation coefficients. Finally, we employ the k-nearest neighbor (KNN) algorithm to classify the sparse representation coefficients, and the final classification label is obtained by voting the KNN results. Experimental results on hyperspectral imagery classification validated the effectiveness of the proposed method.