Hierarchical Discriminative Feature Learning for Hyperspectral Image Classification

Building effective image representations from hyperspectral data helps to improve the performance for classification. In this letter, we develop a hierarchical discriminative feature learning algorithm for hyperspectral image classification, which is a deformation of the spatial-pyramid-matching model based on the sparse codes learned from the discriminative dictionary in each layer of a two-layer hierarchical scheme. The pooling features achieved by the proposed method are more robust and discriminative for the classification. We evaluate the proposed method on two hyperspectral data sets: Indiana Pines and Salinas scene. The results show our method possessing state-of-the-art classification accuracy.