Collaborative Discriminative Manifold Embedding for Hyperspectral Imagery

Abstract

Based on collaborative representation, a novel supervised dimensionality reduction method called collaborative discriminative manifold embedding (CDME) is proposed for hyperspectral imagery. In the proposed CDME, we construct both an intraclass manifold graph and an interclass manifold graph based on two structured dictionaries. In the intraclass manifold graph, the neighborhood points are selected from the dictionary with the same class. Interclass manifold graph calculates the edge weight using all points that are sampled from the dictionary with the different classes. The goal of CDME is to learn a low-dimensional feature space by preserving the intraclass reconstructive structure and the interclass geometric structure simultaneously. Finally, the 1-NN classifier is employed to verify the performance of the CDME. Experimental results demonstrate that CDME outperforms other state-of-the-art dimensionality reduction methods.