Nonnegative-Matrix-Factorization-Based Hyperspectral Unmixing With Partially Known Endmembers

Hyperspectral unmixing is an important technique for estimating fractions of various materials from remote sensing imagery. Most unmixing methods make the assumption that no prior knowledge of endmembers is available before the estimation. This is, however, not true for some unmixing tasks for which part of the endmember signatures may be known in advance. In this paper, we address the hyperspectral unmixing problem with partially known endmembers. We extend nonnegative-matrix-factorization-based unmixing algorithms to incorporate prior information into their models. The proposed approach uses the spectral signature of known endmembers as a constraint, among others, in the unmixing model, and propagates the knowledge by an optimization process which minimizes the difference between the image data and the prior knowledge. Results on both synthetic and real data have validated the effectiveness of the proposed method and have shown that it has outperformed several state-of-the-art methods that use or do not use prior knowledge of endmembers.