Unmixing method for hyperspectral data based on sub-space method with learning process

An unmixing method for hyperspectral Earth observation satellite imagery data is proposed. It is based on a sub-space method with learning process. The proposed method utilizes a sub-space for feature space during unmixing. It is used to be done in a feature space which consists of spectral bands of...

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Bibliographic Details
Published in:Advances in space research 2009-08, Vol.44 (4), p.517-523
Main Authors: Arai, Kohei, Chen, Huahui
Format: Article
Language:English
Subjects:
Astronomy
AVIRIS
Category decomposition
Earth, ocean, space
Exact sciences and technology
External geophysics
Hyperspectral data
Learning process
Sub-space method
Unmixing
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Summary:An unmixing method for hyperspectral Earth observation satellite imagery data is proposed. It is based on a sub-space method with learning process. The proposed method utilizes a sub-space for feature space during unmixing. It is used to be done in a feature space which consists of spectral bands of observation vectors. As the results from the experiments with airborne based hyperspectral imagery data, AVIRIS, it is found that the proposed unmixing is superior to the other existing method in terms of decomposition accuracy and the process time required for the decompositions.
ISSN:0273-1177
1879-1948
DOI:10.1016/j.asr.2009.04.034