Nonlinear Spectral Unmixing for Optimizing Per-pixel Endmember Sets

Nonlinear Spectral Unmixing for Optimizing Per-pixel Endmember Sets

For a given pixel, fractional abundances predicted by spectral mixture analysis (SMA) are most accurate when only the endmembers that comprise it are used. This paper presents a support vector machines (SVM) method to achieve land use/land cover fractions of remote sensing image using two steps: ①de...

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Bibliographic Details
Main Authors: LI Hui, ZHANG Jinqu, CAO Yang, WANG Xingfang
Format: Article
Language:zho
Published: Surveying and Mapping Press 2016-01-01
Series:Acta Geodaetica et Cartographica Sinica
Subjects:
remote sensing image
pixel unmixing
selective endmember
support vector machines (SVM)
nonlinear unmixing
Online Access:http://html.rhhz.net/CHXB/html/2016-1-80.htm

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http://html.rhhz.net/CHXB/html/2016-1-80.htm

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