A Non-Negative Matrix Factorization Approach For Hyperspectral Unmixing With Partial Known Endmembers

In this paper, the ground truth information is introduced to improve the accuracy of hyperspectral unmixing based on non-negative matrix factorization. Specifically, the partial known endmembers which could be surveyed is introduced in NMF model. The relationship between the known and unknown endmembers are explored. The distance function is designed to describe the relationship and combined with NMF model. In this way, the new proposed NMF approach, called PENMF, could use the known endmembers to help estimating the unknown endmembers, so that accurate and robust results can be obtained. The proposed algorithm was compared with NMFupk, which also considered partial known endmembers, using extensive synthetic data and real hyperspectral data. The experiments show that the proposed algorithm can give a better performance.