A manifold Hessian-regularized NMF for hyperspectral data unmixing. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Peng, Tianliang; Tan, Wenqun
• Is Part Of: Remote sensing letters. Volume 11:Issue 1(2020)
• Page Start: 86
• Page End: 95
• Publication Date: 2020-01-02
• Subjects: Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2019.1678792
• Languages: English
• ISSNs: 2150-704X

Abstract

ABSTRACT: Hyperspectral unmixing (HU) has drawn remarkable attention because it can decompose mixed pixels into a set of endmembers and abundance fractions. And the nonnegative matrix factorization (NMF) algorithm has been widely used for solving hyperspectral spectral unmixing problem. This letter proposes a Hessian graph regularized NMF (HGNMF) algorithm, which relies upon the construction of a Hessian graph representation, to solve the hyperspectral unmixing problem. According to the HGNMF algorithm, the smoothness in the estimated abundance maps is well promoted. Moreover, the optimized problem of HGNMF algorithm is also solved by employing the multiplicative updating rules. Compared to other algorithms, the proposed HGNMF algorithm demonstrates lots of advantages based on the simulated results of the synthetic data and real data sets.