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The code in this package implements the Subspace-based Nonlocal Low Rank and Sparse Factorization (SNLRSF) model for Hyperspectral Image Denoising as described in the following paper:

C. Cao, J. Yu, C. Zhou, K. Hu, F. Xiao and X. Gao, "Hyperspectral Image Denoising via Subspace-Based Nonlocal Low-Rank and Sparse Factorization," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 3, pp. 973-988, March 2019.

doi: 10.1109/JSTARS.2019.2896031

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8653489&isnumber=8675414

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Overview

The function "Demo_Generate_Simulated_Data" used to generate simulated data under different Case The function "Demo_SNLRSF" demonstrates simulated data denoising

Data

Please download the data from corresponding addresses.

1. Washington DC: https://engineering.purdue.edu/biehl/MultiSpec/hyperspectral.
2. Pavia University: http://www.ehu.es/ccwintco/index.php
3. Indian_pines: http://www.ehu.eus/ccwintco/index.php
4. Urban: http://www.erdc.usace.army.mil/Media/Fact-Sheets/Fact-Sheet-Article-View/Article/610433/hypercube/
5. EO-1 Hyperion: http://www.lmars.whu.edu.cn/prof_web/zhanghongyan/resource/noise_EOI.zip

Contact

If you have any questions or suggestions with the code, or find a bug, please let us know. Contact Jie Yu at yujieahaq@163.com