Dual Graph Regularized NMF for Hyperspectral Unmixing

Abstract

Hyperspectral unmixing is an important technique for estimating fraction of different land cover types from remote sensing imagery. In recent years, nonnegative matrix factorization (NMF) with various constraints have been introduced into hyperspectral unmixing. Among these methods, graph based constraint have been proved to be useful in capturing the latent manifold structure of the hyperspectral data in the feature space. In this paper, we propose to integrate graph-based constraints based on manifold assumption in feature spaces and consistency of spatial space to regularize the NMF method. Results on both synthetic and real data have validated the effectiveness of the proposed method.