Linear Mixture Analysis for Hyperspectral Imagery in the Presence of Less Prevalent Materials

摘要

Endmember extraction is an important and challenging step to solve the spectral unmixing problem. Most existing endmember extraction algorithms (EEAs) usually find image pixels as endmembers assuming the presence of pure pixels in an image scene or generate virtual endmembers without pure-pixel assumption. When some prevalent materials have pure-pixel representation and pure pixels of other less prevalent materials are absent in the image, it would be more appropriate to extract the endmembers of both prevalent and less prevalent materials, respectively. Therefore, a novel two-stage EEA is presented in this paper. In the first stage, conventional pure-pixel-based EEAs are applied to generate a candidate pixel set, and then spatial information of the candidate pixels is exploited to determine the endmembers of prevalent materials. In the second stage, given known endmembers of prevalent materials, a modified algorithm based on nonnegative matrix factorization is performed to generate the endmembers of less prevalent materials. The validity of the proposed algorithm is demonstrated by experiments based on synthetic mixtures and a real image scene.