Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 402910
Title Minimum volume simplicial enclosure for spectral unmixing of remotely sensed hyperspectral data
Author(s) Hendrix, E.M.T.; García, I.; Plaza, J.; Plaza, A.
Source In: Proceedings 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010), Honolulu, Hawaii, USA, 25-30 July 2010. - Hawaii (USA) : IEEE - ISBN 9781424495665 - p. 193 - 196.
Event Hawaii (USA) : IEEE - ISBN 9781424495665 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010), Honolulu, Hawaii, USA, 2010-07-25/2010-07-30
Department(s) Operations Research and Logistics
Publication type Contribution in proceedings
Publication year 2010
Abstract Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. Linear spectral unmixing relies on two main steps: 1) identification of pure spectral constituents (endmembers), and 2) end member abundance estimation in mixed pixels. One of the main problems concerning the identification of spectral endmembers is the lack of pure spectral signatures in real hyperspectral data due to spatial resolution and mixture phenomena happening at different scales. In this paper, we present a new method for endmember estimation which does not assume the presence of pure pixels in the input data. The method minimizes the volume of an enclosing simplex in the reduced space while estimating the fractional abundance of vertices in simultaneous fashion, as opposed to other volume-based approaches such as N-FINDR which inflate the simplex of maximumvolume that can be formed using available image pixels. Our experimental results and comparisons to other endmember extraction algorithms indicate promising performance of the method in the task of extracting endmembers from real hyperspectral data. In our experiments, we use laboratory-simulated forest scenes with known endmembers and fractional abundances due to their acquisition in a controlled environment using a real hyperspectral imaging instrument
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