Multiscale Context-aware Ensemble Deep KELM for Efficient Hyperspectral Image Classification, TGRS, 2020.

Bobo Xi, Jiaojiao Li, Yunsong Li, Rui song, Weiwei Sun and Qian Du.

Code the for paper: Multiscale Context-aware Ensemble Deep KELM for Efficient Hyperspectral Image Classification.

Fig. 1: Illustration of the proposed multiscale context-aware ensemble deep KELM frameworks. Weighted output layer fusion strategy (WOLFS).

Training and Test Process
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Please simply run the ‘run_demo.m’ to reproduce our MSC-EDKELM results on IndianPines dataset. You can obtain the classification accuracies (15 training samples per class) and the corresponding classification maps shown below. We have successfully test it on Ubuntu 16.04 and Windows systems with Matlab R2017b.

Fig. 2: The composite false-color image, groundtruth, training samples, and classification map of Indian Pines dataset.

References

If you find this code helpful, please kindly cite:

[1] B. Xi, J. Li, Y. Li, R. Song, W. Sun and Q. Du, “Multiscale Context-aware Ensemble Deep KELM for Efficient Hyperspectral Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 6, pp. 5114-5130, June 2021. DOI: 10.1109/TGRS.2020.3022029.
[2] Li, J.; Xi, B.; Du, Q.; Song, R.; Li, Y.; Ren, G. Deep Kernel Extreme-Learning Machine for the Spectral–Spatial Classification of Hyperspectral Imagery. Remote Sens. 2018, 10, 2036. DOI: 10.3390/rs10122036.

Citation Details

BibTeX entry:

@ARTICLE{Xi_TGRS2020_EDKELM,
 author={Xi, Bobo and Li, Jiaojiao and Li, Yunsong and Song, Rui and Sun, Weiwei and Du, Qian},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Multiscale Context-Aware Ensemble Deep KELM for Efficient Hyperspectral Image Classification}, 
  year={2021},
  volume={59},
  number={6},
  pages={5114-5130},
  doi={10.1109/TGRS.2020.3022029}}

@article{Li_RS2018_DKELM,   
title={Deep Kernel Extreme-Learning Machine for the Spectral–Spatial Classification of Hyperspectral Imagery},   
volume={10}, ISSN={2072-4292},   
url={http://dx.doi.org/10.3390/rs10122036},   
DOI={10.3390/rs10122036},   
number={12}, journal={Remote Sensing}, publisher={MDPI AG},   
author={Li, Jiaojiao and Xi, Bobo and Du, Qian and Song, Rui and Li, Yunsong and Ren, Guangbo},   
year={2018},month={Dec},pages={2036}}

Licensing

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3 of the License.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program.