This is an implementation of Hyperspectral Band Selection via Adaptive Subspace Partition Strategy.
Three public datasets, i.e., Indian Pines, Pavia University， and Salinas, are employed to verify the effectiveness of the proposed ASPS_MN.
MATLAB
With respect to ASPS_MN algorithm, to run the code, please perform 'main.m'.
To qualitatively measure the proposed ASPS_MN, four classifiers are employed to verify the effectiveness of the algorithm.
| Dataset | TOF | MDSR | WaLuDi | RMBS | UBS | ASPS_MN (10%) | ASPS_MN (100%) |
|---|---|---|---|---|---|---|---|
| Indian Pines (15 bands) | 0.649 | 0.205 | 7.507 | 43.618 | 0.009 | 0.915 | 6.785 |
| Pavia University (10 bands) | 0.741 | 0.208 | 26.775 | 200.396 | 0.009 | 0.895 | 3.440 |
| Salinas (15 bands) | 1.356 | 0.313 | 40.357 | 265.555 | 0.003 | 1.128 | 5.884 |
Please consider cite this paper if you find it helpful.
@article{Wang2019Hyper,
title={Hyperspectral Band Selection via Adaptive Subspace Partition Strategy},
author={Q. Wang, Q. Li, and X. Li},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
year={2019},
DOI={10.1109/JSTARS.2019.2941454},
publisher={IEEE}
}
If you has any questions, please send e-mail to [email protected].