sanwen211314 / mstv-noise-robust-hyperspectral-image-classification-via-multi-scale-total-variation Goto Github PK

%  Generate the classification results of MSTV method as follows:
%
% P. Duan et al.,"Noise-Robust Hyperspectral Image Classification via Multi-Scale Total Variation", accepted to 
% IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019.
%
% URL: https://ieeexplore.ieee.org/document/8725896
% The SVM toolbox which can be downloaded at:
% http://www.csie.ntu.edu.tw/~cjlin/libsvm/

The code is the implentation of paper 
Puhong Duan, Xudong Kang, Shutao Li, and Pedram Ghamisi,¡°Noise-Robust Hyperspectral Image Classification via Multi-Scale Total Variation¡± IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, to be published, 2019£¬DOI: 10.1109/JSTARS.2019.2915272. 

If you use this code, please kindly cite the paper: 
@ARTICLE{8725896, 
author={P. {Duan} and X. {Kang} and S. {Li} and P. {Ghamisi}}, 
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, 
title={Noise-Robust Hyperspectral Image Classification via Multi-Scale Total Variation}, 
year={2019}, 
volume={}, 
number={}, 
pages={1-15}, 
keywords={Feature extraction;Hyperspectral imaging;Kernel;Principal component analysis;Noise robustness;Dimension reduction;hyperspectral image (HSI) classification;kernel principal component analysis (KPCA);multi-scale total variation (MSTV)}, 
doi={10.1109/JSTARS.2019.2915272}, 
ISSN={1939-1404}, 
month={},}

The code has not been well organized. Please contact me if you meet any problems.

Email: [email protected]