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Proceedings Paper

A fast fully constrained geometric unmixing of hyperspectral images
Author(s): Xin Zhou; Xiao-run Li; Jian-tao Cui; Liao-ying Zhao; Jun-peng Zheng
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Paper Abstract

A great challenge in hyperspectral image analysis is decomposing a mixed pixel into a collection of endmembers and their corresponding abundance fractions. This paper presents an improved implementation of Barycentric Coordinate approach to unmix hyperspectral images, integrating with the Most-Negative Remove Projection method to meet the abundance sum-to-one constraint (ASC) and abundance non-negativity constraint (ANC). The original barycentric coordinate approach interprets the endmember unmixing problem as a simplex volume ratio problem, which is solved by calculate the determinants of two augmented matrix. One consists of all the members and the other consist of the to-be-unmixed pixel and all the endmembers except for the one corresponding to the specific abundance that is to be estimated. In this paper, we first modified the algorithm of Barycentric Coordinate approach by bringing in the Matrix Determinant Lemma to simplify the unmixing process, which makes the calculation only contains linear matrix and vector operations. So, the matrix determinant calculation of every pixel, as the original algorithm did, is avoided. By the end of this step, the estimated abundance meet the ASC constraint. Then, the Most-Negative Remove Projection method is used to make the abundance fractions meet the full constraints. This algorithm is demonstrated both on synthetic and real images. The resulting algorithm yields the abundance maps that are similar to those obtained by FCLS, while the runtime is outperformed as its computational simplicity.

Paper Details

Date Published: 18 November 2014
PDF: 7 pages
Proc. SPIE 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V, 92631Z (18 November 2014); doi: 10.1117/12.2068908
Show Author Affiliations
Xin Zhou, Zhejiang Univ. (China)
Xiao-run Li, Zhejiang Univ. (China)
Jian-tao Cui, Zhejiang Univ. (China)
Liao-ying Zhao, Hangzhou Dianzi Univ. (China)
Jun-peng Zheng, Hangzhou Dianzi Univ. (China)

Published in SPIE Proceedings Vol. 9263:
Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V
Allen M. Larar; Makoto Suzuki; Jianyu Wang, Editor(s)

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