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Proceedings Paper

An improved maximum simplex volume algorithm to unmixing hyperspectral data
Author(s): Haicheng Qu; Bormin Huang; Junping Zhang; Ye Zhang
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Paper Abstract

The maximum simplex volume algorithm (MSVA) is an automatic endmember extraction method based on geometrical properties of simplex in high-dimensional feature space. By utilizing the relation of volume between a simplex and its corresponding parallelohedron in the high-dimensional space, the algorithm extracts endmembers directly from the initial hyperspectral image in a sequential manner without dimensionality reduction. It is thus considered to have overcome a major drawback of N-FINDR algorithm, which requires the data dimension reduced to one less than the number of the endmembers before endmembers extraction. But the MSVA suffers from excessive computation caused by massive determinant operation in practical application. An improved fast implementation method based on partitioned determinant operation is proposed in this paper to reduce computational complexity. Experimental results demonstrate that the proposed fast algorithm can greatly reduce computational complexity, while simultaneously their computational accuracyremains as good as its original algorithm.

Paper Details

Date Published: 23 October 2013
PDF: 7 pages
Proc. SPIE 8895, High-Performance Computing in Remote Sensing III, 889507 (23 October 2013); doi: 10.1117/12.2034759
Show Author Affiliations
Haicheng Qu, Harbin Institute of Technology (China)
Liaoning Technical Univ. (China)
Bormin Huang, Univ. of Wisconsin-Madison (United States)
Junping Zhang, Harbin Institute of Technology (China)
Ye Zhang, Harbin Institute of Technology (China)


Published in SPIE Proceedings Vol. 8895:
High-Performance Computing in Remote Sensing III
Bormin Huang; Antonio J. Plaza; Zhensen Wu, Editor(s)

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