Integrating spatial information in unmixing using the nonnegative matrix factorization

Miguel A. Goenaga-Jimenez; Miguel Vélez-Reyes

doi:10.1117/12.2053401

13 June 2014 Integrating spatial information in unmixing using the nonnegative matrix factorization

Miguel A. Goenaga-Jimenez, Miguel Vélez-Reyes

Proceedings Volume 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX; 908811 (2014) https://doi.org/10.1117/12.2053401
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

An approach to incorporate spatial information in unmixing using the nonnegative matrix factorization is presented. We call this method the spectrally adaptive constrained NMF (sacNMF). The spatial information is incorporated by partitioning hyperspectral images into spectrally homogeneous regions using quadtree region partitioning. Endmembers for each region are extracted using the nonnegative matrix factorization and then clustered in spectral endmembers classes. The endmember classes better account for the variability of spectral endmembers across the landscape. Abundances are estimated using all spectral endmembers. Experimental results using AVIRIS data from Indian Pines is used to demonstrate the potential of the proposed approach. Comparisons with other published approaches are presented.

Citation Download Citation

Miguel A. Goenaga-Jimenez and Miguel Vélez-Reyes "Integrating spatial information in unmixing using the nonnegative matrix factorization", Proc. SPIE 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, 908811 (13 June 2014); https://doi.org/10.1117/12.2053401

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