Geodesic simplex based multiobjective endmember extraction for nonlinear hyperspectral mixtures

This paper presents a novel multiobjective endmember extraction approach for nonlinear hyperspectral mixtures by assuming that the distribution of mixtures conforms to a nonlinear manifold and the endmembers correspond to its extreme points. To identify the endmembers, the approach aims to seek a se...

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Vydané v:	Information sciences Ročník 577; s. 398 - 423
Hlavní autori:	Jiang, Xiangming, Gong, Maoguo, Zhan, Tao, Li, Hao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.10.2021
Predmet:	Boundary detection Geodesic distance Maximum volume Multiobjective endmember extraction Multiple regression Nonlinear manifold Nonlinear manifold Boundary detection Multiobjective endmember extraction Geodesic distance Maximum volume Multiple regression
ISSN:	0020-0255, 1872-6291
On-line prístup:	Získať plný text
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