Endmember generation by projection pursuit

Gregory Solyar; Chein I. Chang; Antonio Plaza

doi:10.1117/12.604137

Endmember generation by projection pursuit

Gregory Solyar
, Chein I. Chang
, Antonio Plaza

Department of Electrical Engineering

Research output: Contribution to journal › Conference article › peer-review

3 Citations (Scopus)

Abstract

Projection pursuit (PP) is an interesting concept, which has been found in many applications. It uses a so-called projection index (PI) as a criterion to seek directions that may lead to interesting findings for data analysts. Unlike the principal components analysis (PCA), which uses variance as a measure to find directions that maximizes data variances, the PI used by the PP finds interesting directions that can be characterized by statistics higher than variance. As a result, the PCA is generally considered as a special case of PP with the PI particularly specified by the variance. Recently, a PP-based approach was developed by Ifarraguerri and Chang for multispectral/hyperspectral image analysis. This paper revisits their approach and investigates its application in endmember generation where endmembers can be extracted from a sequence of projections generated by PP.

Original language	English
Article number	30
Pages (from-to)	288-297
Number of pages	10
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	5806
Issue number	PART I
DOIs	https://doi.org/10.1117/12.604137
Publication status	Published - 2005
Event	Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI - Orlando, FL, United States Duration: 2005 Mar 28 → 2005 Apr 1

All Science Journal Classification (ASJC) codes

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

Access to Document

10.1117/12.604137

Cite this

@article{07f90215f21d4ea681b1a0e22fdc8a03,

title = "Endmember generation by projection pursuit",

abstract = "Projection pursuit (PP) is an interesting concept, which has been found in many applications. It uses a so-called projection index (PI) as a criterion to seek directions that may lead to interesting findings for data analysts. Unlike the principal components analysis (PCA), which uses variance as a measure to find directions that maximizes data variances, the PI used by the PP finds interesting directions that can be characterized by statistics higher than variance. As a result, the PCA is generally considered as a special case of PP with the PI particularly specified by the variance. Recently, a PP-based approach was developed by Ifarraguerri and Chang for multispectral/hyperspectral image analysis. This paper revisits their approach and investigates its application in endmember generation where endmembers can be extracted from a sequence of projections generated by PP.",

author = "Gregory Solyar and Chang, \{Chein I.\} and Antonio Plaza",

year = "2005",

doi = "10.1117/12.604137",

language = "English",

volume = "5806",

pages = "288--297",

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

issn = "0277-786X",

publisher = "SPIE",

number = "PART I",

note = "Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI ; Conference date: 28-03-2005 Through 01-04-2005",

}

TY - JOUR

T1 - Endmember generation by projection pursuit

AU - Solyar, Gregory

AU - Chang, Chein I.

AU - Plaza, Antonio

PY - 2005

Y1 - 2005

N2 - Projection pursuit (PP) is an interesting concept, which has been found in many applications. It uses a so-called projection index (PI) as a criterion to seek directions that may lead to interesting findings for data analysts. Unlike the principal components analysis (PCA), which uses variance as a measure to find directions that maximizes data variances, the PI used by the PP finds interesting directions that can be characterized by statistics higher than variance. As a result, the PCA is generally considered as a special case of PP with the PI particularly specified by the variance. Recently, a PP-based approach was developed by Ifarraguerri and Chang for multispectral/hyperspectral image analysis. This paper revisits their approach and investigates its application in endmember generation where endmembers can be extracted from a sequence of projections generated by PP.

AB - Projection pursuit (PP) is an interesting concept, which has been found in many applications. It uses a so-called projection index (PI) as a criterion to seek directions that may lead to interesting findings for data analysts. Unlike the principal components analysis (PCA), which uses variance as a measure to find directions that maximizes data variances, the PI used by the PP finds interesting directions that can be characterized by statistics higher than variance. As a result, the PCA is generally considered as a special case of PP with the PI particularly specified by the variance. Recently, a PP-based approach was developed by Ifarraguerri and Chang for multispectral/hyperspectral image analysis. This paper revisits their approach and investigates its application in endmember generation where endmembers can be extracted from a sequence of projections generated by PP.

UR - https://www.scopus.com/pages/publications/27544451702

UR - https://www.scopus.com/pages/publications/27544451702#tab=citedBy

U2 - 10.1117/12.604137

DO - 10.1117/12.604137

M3 - Conference article

AN - SCOPUS:27544451702

SN - 0277-786X

VL - 5806

SP - 288

EP - 297

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

IS - PART I

M1 - 30

T2 - Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI

Y2 - 28 March 2005 through 1 April 2005

ER -

Endmember generation by projection pursuit

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this