NDLI: A sparse multiple endmember spectral mixture analysis algorithm of hyperspectral image

Content Provider	IEEE Xplore Digital Library
Author	Zhao, Chun-hui Cui, Shi-ling Qi, Bin
Copyright Year	2014
Description	Author affiliation: College of Information and Communication Engineering, Harbin Engineering University, China (Zhao, Chun-hui; Cui, Shi-ling; Qi, Bin)
Abstract	In the traditional linear spectral mixture model, a class is represented by a single endmember. However, the intra-class spectral variability is usually large, so an endmember is difficult to portray a category accurately, leading to incorrect unmixing results. Some algorithms play a positive role in overcoming the endmember variability, but there are shortcomings on computation intensive, unsatisfactory unmixing results and so on. For these issues, we have proposed a sparse multiple endmember spectral mixture analysis algorithm (SMESMA). First determine the intra-class spectra of all the feature classes for each pixel using orthogonal matching pursuit algorithm (OMP), then find the optimal number of endmember combinations according to the relative increase in root-mean-square error to avoid over-fitting. Synthetic and real data experiments show that the SMESMA unmixing results are ideal comparatively and the abundance error is the lowest among the five methods and multiple endmember spectral mixture analysis is more reasonable.
Starting Page	687
Ending Page	692
File Size	888025
Page Count	6
File Format	PDF
ISBN	9781479921881
ISSN	21645221
e-ISBN	9781479921867
DOI	10.1109/ICOSP.2014.7015091
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Hyperspectral imaging Matching pursuit algorithms Signal to noise ratio Mathematical model Computational modeling intra-class spectral variability Hyperspectral image processing sparse representation multiple endmember spectral unmixing OMP
Content Type	Text
Resource Type	Article

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