Robust unmixing algorithms for hyperspectral imagery

Abderrahim Halimi, Yoann Altmann, Gerald Stuart Buller, Stephen McLaughlin, William Oxford, Damien Clarke, Jonathan Piper

Research output: Contribution to conferencePaper

Abstract

The linear mixture model (LMM) assumes a hyperspectral pixel
spectrum to be a linear combination of endmember spectra corrupted
by additive noise. This model is widely used for spectral
unmixing mainly because of its simplicity. However, the LMM
can be inappropriate in presence of nonlinear effects, endmember
variability or outliers. This paper presents a comparison between recent
robust hyperspectral unmixing algorithms. The mixture models
are first introduced followed by the description of their associated
unmixing algorithms. The algorithms are then analyzed when
considering a real image acquired over the region of Porton Down
in England. The results discuss the behavior of each algorithm to
unmix these data and compare their ability to detect the natural or
man-made outliers in the scene. The obtained results highlight the
potential of the studied mixture models to overcome the current
limitations of the LMM.
Original languageEnglish
Publication statusAccepted/In press - 16 Jun 2016
Event6th Sensor Signal Processing for Defence Conference 2016 - Edinburgh, United Kingdom
Duration: 22 Sep 201623 Sep 2016
Conference number: 6th

Conference

Conference6th Sensor Signal Processing for Defence Conference 2016
Abbreviated titleSSPD 2016
CountryUnited Kingdom
CityEdinburgh
Period22/09/1623/09/16

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Cite this

Halimi, A., Altmann, Y., Buller, G. S., McLaughlin, S., Oxford, W., Clarke, D., & Piper, J. (Accepted/In press). Robust unmixing algorithms for hyperspectral imagery. Paper presented at 6th Sensor Signal Processing for Defence Conference 2016, Edinburgh, United Kingdom.
Halimi, Abderrahim ; Altmann, Yoann ; Buller, Gerald Stuart ; McLaughlin, Stephen ; Oxford, William ; Clarke, Damien ; Piper, Jonathan. / Robust unmixing algorithms for hyperspectral imagery. Paper presented at 6th Sensor Signal Processing for Defence Conference 2016, Edinburgh, United Kingdom.
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abstract = "The linear mixture model (LMM) assumes a hyperspectral pixelspectrum to be a linear combination of endmember spectra corruptedby additive noise. This model is widely used for spectralunmixing mainly because of its simplicity. However, the LMMcan be inappropriate in presence of nonlinear effects, endmembervariability or outliers. This paper presents a comparison between recentrobust hyperspectral unmixing algorithms. The mixture modelsare first introduced followed by the description of their associatedunmixing algorithms. The algorithms are then analyzed whenconsidering a real image acquired over the region of Porton Downin England. The results discuss the behavior of each algorithm tounmix these data and compare their ability to detect the natural orman-made outliers in the scene. The obtained results highlight thepotential of the studied mixture models to overcome the currentlimitations of the LMM.",
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year = "2016",
month = "6",
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note = "6th Sensor Signal Processing for Defence Conference 2016, SSPD 2016 ; Conference date: 22-09-2016 Through 23-09-2016",

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Halimi, A, Altmann, Y, Buller, GS, McLaughlin, S, Oxford, W, Clarke, D & Piper, J 2016, 'Robust unmixing algorithms for hyperspectral imagery' Paper presented at 6th Sensor Signal Processing for Defence Conference 2016, Edinburgh, United Kingdom, 22/09/16 - 23/09/16, .

Robust unmixing algorithms for hyperspectral imagery. / Halimi, Abderrahim; Altmann, Yoann; Buller, Gerald Stuart; McLaughlin, Stephen; Oxford, William; Clarke, Damien; Piper, Jonathan.

2016. Paper presented at 6th Sensor Signal Processing for Defence Conference 2016, Edinburgh, United Kingdom.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Robust unmixing algorithms for hyperspectral imagery

AU - Halimi, Abderrahim

AU - Altmann, Yoann

AU - Buller, Gerald Stuart

AU - McLaughlin, Stephen

AU - Oxford, William

AU - Clarke, Damien

AU - Piper, Jonathan

PY - 2016/6/16

Y1 - 2016/6/16

N2 - The linear mixture model (LMM) assumes a hyperspectral pixelspectrum to be a linear combination of endmember spectra corruptedby additive noise. This model is widely used for spectralunmixing mainly because of its simplicity. However, the LMMcan be inappropriate in presence of nonlinear effects, endmembervariability or outliers. This paper presents a comparison between recentrobust hyperspectral unmixing algorithms. The mixture modelsare first introduced followed by the description of their associatedunmixing algorithms. The algorithms are then analyzed whenconsidering a real image acquired over the region of Porton Downin England. The results discuss the behavior of each algorithm tounmix these data and compare their ability to detect the natural orman-made outliers in the scene. The obtained results highlight thepotential of the studied mixture models to overcome the currentlimitations of the LMM.

AB - The linear mixture model (LMM) assumes a hyperspectral pixelspectrum to be a linear combination of endmember spectra corruptedby additive noise. This model is widely used for spectralunmixing mainly because of its simplicity. However, the LMMcan be inappropriate in presence of nonlinear effects, endmembervariability or outliers. This paper presents a comparison between recentrobust hyperspectral unmixing algorithms. The mixture modelsare first introduced followed by the description of their associatedunmixing algorithms. The algorithms are then analyzed whenconsidering a real image acquired over the region of Porton Downin England. The results discuss the behavior of each algorithm tounmix these data and compare their ability to detect the natural orman-made outliers in the scene. The obtained results highlight thepotential of the studied mixture models to overcome the currentlimitations of the LMM.

M3 - Paper

ER -

Halimi A, Altmann Y, Buller GS, McLaughlin S, Oxford W, Clarke D et al. Robust unmixing algorithms for hyperspectral imagery. 2016. Paper presented at 6th Sensor Signal Processing for Defence Conference 2016, Edinburgh, United Kingdom.