Band selection for hyperspectral images using copulas-based mutual information

Xuexing Zeng; T. S. Durrani

doi:10.1109/SSP.2009.5278570

Band selection for hyperspectral images using copulas-based mutual information

Xuexing Zeng, T. S. Durrani

Electronic And Electrical Engineering

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

1 Citation (Scopus)

Abstract

This paper explores a new measure for band selection of hyperspectral images using copulas-based mutual information. Mutual information offers a measure of the dependence between random variables, which can be used to select specific bands for the analysis of hyperspectral images. This is achieved by comparing mutual information values between the band images and a reference map. In this paper, copula density functions are exploited for the estimation of mutual information between the images. Due to the special relationship between copula density functions and joint probability density functions, copulas offer a natural and robust way for the estimation of the mutual information.

Original language	English
Title of host publication	Statistical Signal Processing, 2009. SSP '09. IEEE/SP 15th Workshop on
Publisher	IEEE
Pages	341-344
Number of pages	3
ISBN (Print)	978-1-4244-2709-3
DOIs	https://doi.org/10.1109/SSP.2009.5278570
Publication status	Published - 6 Oct 2009
Event	15th IEEE International Statistical Signal Procecssing Workshop, Cardiff, Wales - Duration: 1 Jan 1900 → …

Conference

Conference	15th IEEE International Statistical Signal Procecssing Workshop, Cardiff, Wales
Period	1/01/00 → …

Keywords

band selection
Copulas
hyperspectral image
mutual information
bandwidth
density functional theory
entropy
hyperspectral imaging
probability density function
random variables
hyperspectral sensors
image processing
infrared detectors
geophysical signal processing
remote sensing

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10.1109/SSP.2009.5278570

Cite this

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title = "Band selection for hyperspectral images using copulas-based mutual information",

abstract = "This paper explores a new measure for band selection of hyperspectral images using copulas-based mutual information. Mutual information offers a measure of the dependence between random variables, which can be used to select specific bands for the analysis of hyperspectral images. This is achieved by comparing mutual information values between the band images and a reference map. In this paper, copula density functions are exploited for the estimation of mutual information between the images. Due to the special relationship between copula density functions and joint probability density functions, copulas offer a natural and robust way for the estimation of the mutual information.",

keywords = "band selection, Copulas , hyperspectral image, mutual information, bandwidth, density functional theory, entropy, hyperspectral imaging , probability density function, random variables, hyperspectral sensors, image processing, infrared detectors, geophysical signal processing , remote sensing",

author = "Xuexing Zeng and Durrani, {T. S.}",

note = "e-isbn: 978-1-4244-2711-6; 15th IEEE International Statistical Signal Procecssing Workshop, Cardiff, Wales ; Conference date: 01-01-1900",

year = "2009",

month = oct,

day = "6",

doi = "10.1109/SSP.2009.5278570",

language = "English",

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T1 - Band selection for hyperspectral images using copulas-based mutual information

AU - Zeng, Xuexing

AU - Durrani, T. S.

N1 - e-isbn: 978-1-4244-2711-6

PY - 2009/10/6

Y1 - 2009/10/6

N2 - This paper explores a new measure for band selection of hyperspectral images using copulas-based mutual information. Mutual information offers a measure of the dependence between random variables, which can be used to select specific bands for the analysis of hyperspectral images. This is achieved by comparing mutual information values between the band images and a reference map. In this paper, copula density functions are exploited for the estimation of mutual information between the images. Due to the special relationship between copula density functions and joint probability density functions, copulas offer a natural and robust way for the estimation of the mutual information.

AB - This paper explores a new measure for band selection of hyperspectral images using copulas-based mutual information. Mutual information offers a measure of the dependence between random variables, which can be used to select specific bands for the analysis of hyperspectral images. This is achieved by comparing mutual information values between the band images and a reference map. In this paper, copula density functions are exploited for the estimation of mutual information between the images. Due to the special relationship between copula density functions and joint probability density functions, copulas offer a natural and robust way for the estimation of the mutual information.

KW - band selection

KW - Copulas

KW - hyperspectral image

KW - mutual information

KW - bandwidth

KW - density functional theory

KW - entropy

KW - hyperspectral imaging

KW - probability density function

KW - random variables

KW - hyperspectral sensors

KW - image processing

KW - infrared detectors

KW - geophysical signal processing

KW - remote sensing

UR - http://dx.doi.org10.1109/SSP.2009.5278570

U2 - 10.1109/SSP.2009.5278570

DO - 10.1109/SSP.2009.5278570

M3 - Conference contribution book

SN - 978-1-4244-2709-3

SP - 341

EP - 344

BT - Statistical Signal Processing, 2009. SSP '09. IEEE/SP 15th Workshop on

PB - IEEE

T2 - 15th IEEE International Statistical Signal Procecssing Workshop, Cardiff, Wales

Y2 - 1 January 1900

ER -

Band selection for hyperspectral images using copulas-based mutual information

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