Hyperspectral Imagery Further Unmixing Based On Analysis Of Variance
- 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/).
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 -