Kernel fully constrained least squares abundance estimates

Joshua Broadwater; Rama Chellappa; Amit Banerjee; Philippe Burlina

doi:10.1109/IGARSS.2007.4423736

Kernel fully constrained least squares abundance estimates

Joshua Broadwater, Rama Chellappa, Amit Banerjee, Philippe Burlina

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

82 Scopus citations

Abstract

A critical step for fitting a linear mixing model to hyperspectral imagery is the estimation of the abundances. The abundances are the percentage of each endmember within a given pixel; therefore, they should be non-negative and sum to one. With the advent of kernel based algorithms for hyperspectral imagery, kernel based abundance estimates have become necessary. This paper presents such an algorithm that estimates the abundances in the kernel feature space while maintaining the non-negativity and sum-to-one constraints. The usefulness of the algorithm is shown using the AVIRIS Cuprite, Nevada image.

Original language	English (US)
Title of host publication	2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
Pages	4041-4044
Number of pages	4
DOIs	https://doi.org/10.1109/IGARSS.2007.4423736
State	Published - 2007
Externally published	Yes
Event	2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007 - Barcelona, Spain Duration: Jun 23 2007 → Jun 28 2007

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Other

Other	2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
Country/Territory	Spain
City	Barcelona
Period	6/23/07 → 6/28/07

Keywords

Abundance estimates
Hyperspectral imagery
Kernel functions
Spectral unmixing

ASJC Scopus subject areas

Computer Science Applications
Earth and Planetary Sciences(all)

Access to Document

10.1109/IGARSS.2007.4423736

Cite this

Kernel fully constrained least squares abundance estimates. / Broadwater, Joshua; Chellappa, Rama; Banerjee, Amit et al.
2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007. 2007. p. 4041-4044 4423736 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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

Broadwater, J, Chellappa, R, Banerjee, A & Burlina, P 2007, Kernel fully constrained least squares abundance estimates. in 2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007., 4423736, International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4041-4044, 2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007, Barcelona, Spain, 6/23/07. https://doi.org/10.1109/IGARSS.2007.4423736

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pages = "4041--4044",

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AU - Broadwater, Joshua

AU - Chellappa, Rama

AU - Banerjee, Amit

AU - Burlina, Philippe

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AB - A critical step for fitting a linear mixing model to hyperspectral imagery is the estimation of the abundances. The abundances are the percentage of each endmember within a given pixel; therefore, they should be non-negative and sum to one. With the advent of kernel based algorithms for hyperspectral imagery, kernel based abundance estimates have become necessary. This paper presents such an algorithm that estimates the abundances in the kernel feature space while maintaining the non-negativity and sum-to-one constraints. The usefulness of the algorithm is shown using the AVIRIS Cuprite, Nevada image.

KW - Abundance estimates

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KW - Kernel functions

KW - Spectral unmixing

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T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

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ER -

Kernel fully constrained least squares abundance estimates

Abstract

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Keywords

ASJC Scopus subject areas

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