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• W2999903818 abstract "Nonlinear geometric manifold of hyperspectral data usually makes great trouble for accurate endmember extraction in literature. To address this issue, we propose a novel nonlinear endmember extraction algorithm by building a hypergraph and a fuzzy assessment strategy. The global change of nonlinear data manifold is first measured in a hypergraph whose hyperedges correspond to different local pixel subgroups. In contrast to edges in a simple graph, every hyperpath connected by multiple hyperedges instead of individual pixels effectively facilitates the determination of the simplex spanned by endmembers on complex data manifolds interfered by noises and outliers. Furthermore, in the hypergraph-based manifold system, a reliable fuzzy assessment mechanism for extracting the final endmembers is established by combining the classic simplex volume maximization rule with the inherent properties of hyperspectral data and hypergraph. In this procedure, the impact of outliers and the complex geometric structures of data can be further effectively reduced, leading to better unmixing results. Experimental results of various types of simulated data and real hyperspectral imagery demonstrate that the proposed algorithm (hypergraph and fuzzy-assessment-based nonlinear endmember extraction) can extract more accurate endmembers from complex data manifolds, and it is more robust to noises and outliers compared with state-of-the-art algorithms." @default.
• W2999903818 created "2020-01-23" @default.
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• W2999903818 creator A5046815454 @default.
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• W2999903818 date "2020-01-01" @default.
• W2999903818 modified "2023-10-12" @default.
• W2999903818 title "Nonlinear Endmember Identification for Hyperspectral Imagery via Hyperpath-Based Simplex Growing and Fuzzy Assessment" @default.
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• W2999903818 doi "https://doi.org/10.1109/jstars.2019.2962609" @default.
• W2999903818 hasPublicationYear "2020" @default.
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