Kernel-based weighted abundance constrained linear spectral mixture analysis

Keng Hao Liu; Englin Wong; Chein I. Chang

doi:10.1117/12.884650

Kernel-based weighted abundance constrained linear spectral mixture analysis

Keng Hao Liu, Englin Wong, Chein I. Chang

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Linear Spectral Mixture Analysis (LSMA) is a theory developed to perform spectral unmixing where three major LSMA techniques, Least Squares Orthogonal Subspace Projection (LSOSP), Non-negativity Constrained Least Squares (NCLS) and Fully Constrained Least Squares (FCLS) for this purpose. Later on these three techniques were further extended to Fisher's LSMA (FLSMA), Weighted Abundance Constrained-LSMA (WAC-LSMA) and kernel-based LSMA (KLSMA). This paper combines both approaches of KLSMA and WACLSMA to derive a most general version of LSMA, Kernel-based WACLSMA (KWAC-LSMA) which includes all the above-mentioned LSMAs as its special cases. The utility of the KWAC-LSMA is further demonstrated by multispectral and hyperspectral experiments for performance analysis.

Original language	English
Title of host publication	Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
DOIs	https://doi.org/10.1117/12.884650
Publication status	Published - 2011
Event	Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII - Orlando, FL, United States Duration: 2011 Apr 25 → 2011 Apr 28

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	8048
ISSN (Print)	0277-786X

Conference

Conference	Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
Country/Territory	United States
City	Orlando, FL
Period	11-04-25 → 11-04-28

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

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10.1117/12.884650

Cite this

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title = "Kernel-based weighted abundance constrained linear spectral mixture analysis",

abstract = "Linear Spectral Mixture Analysis (LSMA) is a theory developed to perform spectral unmixing where three major LSMA techniques, Least Squares Orthogonal Subspace Projection (LSOSP), Non-negativity Constrained Least Squares (NCLS) and Fully Constrained Least Squares (FCLS) for this purpose. Later on these three techniques were further extended to Fisher's LSMA (FLSMA), Weighted Abundance Constrained-LSMA (WAC-LSMA) and kernel-based LSMA (KLSMA). This paper combines both approaches of KLSMA and WACLSMA to derive a most general version of LSMA, Kernel-based WACLSMA (KWAC-LSMA) which includes all the above-mentioned LSMAs as its special cases. The utility of the KWAC-LSMA is further demonstrated by multispectral and hyperspectral experiments for performance analysis.",

author = "Liu, {Keng Hao} and Englin Wong and Chang, {Chein I.}",

year = "2011",

doi = "10.1117/12.884650",

language = "English",

isbn = "9780819486226",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

booktitle = "Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII",

note = "Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII ; Conference date: 25-04-2011 Through 28-04-2011",

}

Liu, KH, Wong, E & Chang, CI 2011, Kernel-based weighted abundance constrained linear spectral mixture analysis. in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII., 80481P, Proceedings of SPIE - The International Society for Optical Engineering, vol. 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, Orlando, FL, United States, 11-04-25. https://doi.org/10.1117/12.884650

Kernel-based weighted abundance constrained linear spectral mixture analysis. / Liu, Keng Hao; Wong, Englin; Chang, Chein I.
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII. 2011. 80481P (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8048).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Wong, Englin

AU - Chang, Chein I.

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N2 - Linear Spectral Mixture Analysis (LSMA) is a theory developed to perform spectral unmixing where three major LSMA techniques, Least Squares Orthogonal Subspace Projection (LSOSP), Non-negativity Constrained Least Squares (NCLS) and Fully Constrained Least Squares (FCLS) for this purpose. Later on these three techniques were further extended to Fisher's LSMA (FLSMA), Weighted Abundance Constrained-LSMA (WAC-LSMA) and kernel-based LSMA (KLSMA). This paper combines both approaches of KLSMA and WACLSMA to derive a most general version of LSMA, Kernel-based WACLSMA (KWAC-LSMA) which includes all the above-mentioned LSMAs as its special cases. The utility of the KWAC-LSMA is further demonstrated by multispectral and hyperspectral experiments for performance analysis.

AB - Linear Spectral Mixture Analysis (LSMA) is a theory developed to perform spectral unmixing where three major LSMA techniques, Least Squares Orthogonal Subspace Projection (LSOSP), Non-negativity Constrained Least Squares (NCLS) and Fully Constrained Least Squares (FCLS) for this purpose. Later on these three techniques were further extended to Fisher's LSMA (FLSMA), Weighted Abundance Constrained-LSMA (WAC-LSMA) and kernel-based LSMA (KLSMA). This paper combines both approaches of KLSMA and WACLSMA to derive a most general version of LSMA, Kernel-based WACLSMA (KWAC-LSMA) which includes all the above-mentioned LSMAs as its special cases. The utility of the KWAC-LSMA is further demonstrated by multispectral and hyperspectral experiments for performance analysis.

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ER -

Kernel-based weighted abundance constrained linear spectral mixture analysis

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