We propose a method of hyperspectral unmixing for the linear mixing model (LMM) while both the spectral signatures of endmembers and their fractional abundances are unknown. The proposed algorithm employs the non-negative matrix factorization (NMF) method as well as simultaneous (collaborative) sparse regression model. We formulate the NMF problem along with an averaging over the l2-norm of the fractional abundances so-called l2,q-norm term. We show that this problem can be efficiently solved by using the Karush-Kuhn-Tucker (KKT) conditions. Our simulations show that the proposed algorithm outperforms the state-of-the-art methods in terms of spectral angle distance (SAD) and abundance angle distance (AAD).
CITATION STYLE
Salehani, Y. E., & Gazor, S. (2016). Collaborative unmixing hyperspectral imagery via nonnegative matrix factorization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9680, pp. 118–126). Springer Verlag. https://doi.org/10.1007/978-3-319-33618-3_13
Mendeley helps you to discover research relevant for your work.