RCMF: Robust Constrained Matrix Factorization for Hyperspectral Unmixing

    Research output: Contribution to journalArticle

    14 Citations (Scopus)
    718 Downloads (Pure)

    Abstract

    We propose a constrained matrix factorization approach for linear unmixing of hyperspectral data. Our approach factorizes a hyperspectral cube into its constituent endmembers and their fractional abundances such that the endmembers are sparse nonnegative linear combinations of the observed spectra themselves. The association between the extracted endmembers and the observed spectra is explicitly noted for physical interpretability. To ensure reliable unmixing, we make the matrix factorization procedure robust to outliers in the observed spectra. Our approach simultaneously computes the endmembers and their abundances in an efficient and unsupervised manner. The extracted endmembers are nonnegative quantities, whereas their abundances additionally follow the sum-to-one constraint. We thoroughly evaluate our approach using synthetic data with white and correlated noise as well as real hyperspectral data. Experimental results establish the effectiveness of our approach.

    Original languageEnglish
    Pages (from-to)3354-3366
    Number of pages13
    JournalIEEE Transactions on Geoscience and Remote Sensing
    Volume55
    Issue number6
    DOIs
    Publication statusPublished - Jun 2017

    Cite this