Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

Hyperspectral Imagery Further Unmixing Based On Analysis Of Variance

Authors
Lei Wang, Zhenfeng Shao
Corresponding Author
Lei Wang
Available Online July 2015.
DOI
10.2991/lemcs-15.2015.225How to use a DOI?
Keywords
Hyperspectral imagery; Linear unmixing; Sparse regression; Analysis of variance
Abstract

Hyperspectral imagery unmixing model based on sparse regression uses the existing endmembers’ library as priori information. Usually, the existing endmembers’ library contains almost all kinds of ground objects. Even though sparse regression-based imagery unmixing method added sparse constraint to the original unmxing model, the solution is still far away as sparse as real scenario. Therefore, the authors propose a hyperspectral imagery further unmixing method based on the analysis of variance. In this method, fractional abundances unmixed by sparse regression-based approach are analyzed with t-test. If the fractional abundances are not significant enough, the corresponding endmembers will be removed and a new optimal endmember subset will be extracted. Then the unmixing process was remade with acquired optimal endmember subset and the final result will be acquired. The experimental results indicate that the proposed method could acquire sparser solution, which is closer to the real sparsity of abundance, both in simulate scenario and real scenario. Furthermore, the precision of the endmember recognition of proposed method is more than 97%, which is a pretty good result.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
Series
Advances in Intelligent Systems Research
Publication Date
July 2015
ISBN
978-94-6252-102-5
ISSN
1951-6851
DOI
10.2991/lemcs-15.2015.225How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Lei Wang
AU  - Zhenfeng Shao
PY  - 2015/07
DA  - 2015/07
TI  - Hyperspectral Imagery Further Unmixing Based On Analysis Of Variance
BT  - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
PB  - Atlantis Press
SP  - 1134
EP  - 1140
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-15.2015.225
DO  - 10.2991/lemcs-15.2015.225
ID  - Wang2015/07
ER  -