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Minimum Volume Simplex Analysis: A Fast Algorithm for Linear Hyperspectral Unmixing

delete2015-09-01
delete204
PRE
AI
J
Jun Li *
A
Alexander Agathos
D
Daniela Zaharie
J
José M. Bioucas‐Dias
A
Antonio Plaza
李
李霞 (Xia Li)
DOI:10.1109/TGRS.2015.2417162delete
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Abstract

Abstract

En 中文
Linear spectral unmixing aims at estimating the number of pure spectral substances, also called endmembers, their spectral signatures, and their abundance fractions in remotely sensed hyperspectral images. This paper describes a method for unsupervised hyperspectral unmixing called minimum volume simplex analysis (MVSA) and introduces a new computationally efficient implementation. MVSA approaches hyperspectral unmixing by fitting a minimum volume simplex to the hyperspectral data, constraining the abundance fractions to belong to the probability simplex. The resulting optimization problem, which is computationally complex, is solved in this paper by implementing a sequence of quadratically constrained subproblems using the interior point method, which is particularly effective from the computational viewpoint. The proposed implementation (available online: www.lx.it.pt/%7ejun/DemoMVSA.zip) is shown to exhibit state-of-the-art performance not only in terms of unmixing accuracy, particularly in nonpure pixel scenarios, but also in terms of computational performance. Our experiments have been conducted using both synthetic and real data sets. An important assumption of MVSA is that pure pixels may not be present in the hyperspectral data, thus addressing a common situation in real scenarios which are often dominated by highly mixed pixels. In our experiments, we observe that MVSA yields competitive performance when compared with other available algorithms that work under the nonpure pixel regime. Our results also demonstrate that MVSA is well suited to problems involving a high number of endmembers (i. e., complex scenes) and also for problems involving a high number of pixels (i. e., large scenes).
Keywords:
Endmember identification
hyperspectral imaging
interior point method
minimum volume simplex analysis (MVSA)
spectral unmixing
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Journal

IEEE Transactions on Geoscience and Remote Sensing cover
IEEE Transactions on Geoscience and Remote Sensing
IF:
8.6
Papers:
2.1W
Citations:
10.7W

Organization

U
universidade de lisboa
Scholars:
3.4W
Papers: 3.1W
Citations: 29
W
West University of Timisoara
Scholars:
1.8K
Papers: 1.8K
Citations: 1.1K
S
Sun Yat Sen University
Scholars:
9.9W
Papers: 7.2W
Citations: 95
I
instituto de telecomunicacoes
Scholars:
808
Papers: 852
Citations: 0
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Cited Papers

Cited Papers

A Simplex Volume Maximization Framework for Hyperspectral Endmember Extraction
err2011-11-01
err181
PREAI
errChan, Tsung-Han; Ma, Wing-Kin; Ambikapathi, ArulMurugan; Chi, Chong-Yung
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errSave
Supervised Nonlinear Spectral Unmixing Using a Postnonlinear Mixing Model for Hyperspectral Imagery
err2012-06-01
err217
errOAAI
errAltmann, Yoann; Halimi, Abderrahim; Dobigeon, Nicolas; Tourneret, Jean-Yves
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researcher View more