This code is an implementation of Shape-adaptive Reconstruction (SaR) proposed in "Classification of Hyperspectral Images Using SVM with Shape-adaptive Reconstruction and Smoothed Total Variation", see Link, and "Unsupervised Spatial-spectral Hyperspectral Image Reconstruction and Clustering with Diffusion Geometry", see Link.
The SaR code can be used as a denoising method for remote sensing datasets. In this paper, SaR is firstly used as a preprocessing step before training a semi-supervised classifier. SaR has been applied in an unsupervised diffusion-based algorithm called DSIRC as a smoothing stage as well.
SaR uses several Matlab Toolboxes, such as LASIP and SA-DCT. SVM-STV uses the LIBSVM toolbox to implement SVMs.
Notes:
- The code contains the Shape-adaptive Reconstruction part that use the spatial information to reduce the noise.
- To run a demo of SaR, please run SaR_main.m. Make sure you download the benchmark datasets before trying.
- To run a demo of SaR-SVM-STV (semi-supervised), please run SaR_SVM_STV.m.
- To run a demo of DSIRC (unsupervised), please run DSIRCGS.m. DSIRC uses the D-VIC toolbox for unsupervised diffusion learning.
- To apply the code on your dataset, you could simply change the input datasets.
- Contact: kangnicui2@gmail.com
If you find it useful or use it in any publications, please cite the following papers:
Li, R., Cui, K., Chan, R. H., & Plemmons, R. J.. "Classification of Hyperspectral Images Using SVM with Shape-adaptive Reconstruction and Smoothed Total Variation". in Proc IEEE Int Geosci Remote Sens Symp, IEEE, 2022. Link.
Cui, K., Li, R., Polk, S.L., Murphy, J.M., & Plemmons, R. J., Chan, R. H.. "Unsupervised Spatial-spectral Hyperspectral Image Reconstruction and Clustering with Diffusion Geometry". in Proc IEEE Workshop Hyperspectral Image Signal Process Evol Remote Sens, IEEE, 2022. Link.