fngbs's Introduction

FNGBS

This is an implementation of A Fast Neighborhood Grouping Method for Hyperspectral Band Selection.

Dataset

Four public datasets, i.e., Indian Pines, Botswana, Pavia University， and Salinas, are employed to verify the effectiveness of the proposed FNGBS.

Requirement

MATLAB, libsvm, cruve fitting tool

Implementation

With respect to FNGBS algorithm, to run the code, please perform 'main.m'. As for obtained recommended bands, we need to conduct 'main_recomBand.m'. Note that the hyperparameters of each dataset is set as follows

Dataset	k	Z
Indian Pines	2	10%
Botswana	2	1%
Pavia University	3	1%
Salinas	3	1%

Result

To qualitatively measure the proposed FNGBS, KNN and SVM classifiers are employed to verify the effectiveness of the algorithm.

Recommended Bands Comparison:

Classification Performance Comparison:

Computational Time Comparison

Dataset	E-FDPC	WaLuDi	SNNC	TOF	FNGBS (1%)	FNGBS (100%)
Indian Pines (6 bands)	0.121	7.430	0.4411	0.4165	0.2542	0.2995
Botswana (8 bands)	0.661	99.281	3.738	1.843	0.892	3.442
Pavia University (13 bands)	0.282	27.930	1.201	0.925	0.336	1.421
Salinas (6 bands)	0.381	40.382	1.61	1.276	0.465	1.464

Citation

Please consider cite this paper if you find it helpful.

@article{wang2020afast,

title={A Fast Neighborhood Grouping Method for Hyperspectral Band Selection},
author={Q. Wang, Q. Li and X. Li},
journal={IEEE Transactions on Geoscience and Remote Sensing},
year={2020},
doi={10.1109/TGRS.2020.3011002}
}

If you has any questions, please send e-mail to [email protected].

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