NDLI: Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data

Content Provider	IEEE Xplore Digital Library
Author	Jun Li Bioucas-Dias, J.M.
Copyright Year	2008
Description	Author affiliation: Inst. de Telecomun., Tech. Univ. of Lisbon, Lisbon (Jun Li; Bioucas-Dias, J.M.)
Abstract	This paper presents a new method of minimum volume class for hyperspectral unmixing, termed minimum volume simplex analysis (MVSA). The underlying mixing model is linear; i.e., the mixed hyperspectral vectors are modeled by a linear mixture of the end-member signatures weighted by the correspondent abundance fractions. 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 is solved by implementing a sequence of quadratically constrained subproblems. In a final step, the hard constraint on the abundance fractions is replaced with a hinge type loss function to account for outliers and noise. We illustrate the state-of-the-art performance of the MVSA algorithm in unmixing simulated data sets. We are mainly concerned with the realistic scenario in which the pure pixel assumption (i.e., there exists at least one pure pixel per end member) is not fulfilled. In these conditions, the MVSA yields much better performance than the pure pixel based algorithms.
File Size	456533
File Format	PDF
ISBN	9781424428076
DOI	10.1109/IGARSS.2008.4779330
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Hyperspectral imaging Vectors Source separation Bayesian methods Telecommunications Constraint optimization Fasteners Layout Inference algorithms Hyperspectral unmixing Minimum volume simplex
Content Type	Text
Resource Type	Article

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