This repository contains the Matlab code for the paper Hyperspectral Image Clustering withSpatially-Regularized Ultrametrics
To use our implementation of SRUSC, please cite our paper at: https://arxiv.org/abs/2004.05048
This implementation is partially adapted from the code https://jmurphy.math.tufts.edu/Code/LLPD_SpectralClustering_V2.1.zip. Please also cite the paper: Little, A., Maggioni, M., Murphy, J.M. Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms. Journal of Machine Learning Research, 21(6), pp. 1-66. 2020. https://www.jmlr.org/papers/volume21/18-085/18-085.pdf
Source of the HSI data set: http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Salinas_scene
If you only need our method, please refer to the file in the folder SRUSC/scripts. The two synthetic HSI are FourSpheres and ThreeCubes, and two real HSI are SalinasA and PaviaU.
If you need all the comparisions, please use the older in the folder SRUSC/RunAll. Note: To run all the comparisions, please download the code from the following website:
Diffusion Learning (DL):https://jmurphy.math.tufts.edu/Code/ J.M. Murphy and M. Maggioni. Unsupervised clustering and active learning of hyperspectral images with nonlinear diffusion. IEEE Transactions on Geoscience and Remote Sensing, 57(3):1829–1845, 2019. M. Maggioni and J.M. Murphy. Learning by unsupervised nonlinear diffusion. Journal of Machine Learning Research, 20(160):1–56, 2019.
Clustering by Fast Search and Find of Density Peaks (FSFDPC): https://people.sissa.it/~laio/Research/Res_clustering.php A. Rodriguez and A. Laio. Clustering by fast search and find of densitypeaks.Science, 344(6191):1492–1496, 2014.
Nonnegative Matrix Factorization (NMF): https://sites.google.com/site/nicolasgillis/code N. Gillis, D. Kuang, and H. Park. Hierarchical clustering of hyperspectral images using rank-two nonnegative matrix factorization. IEEE Transactions on Geoscience and Remote Sensing, 53(4):2066–2078, 2015.
Local Covariance Matrix Representation: (LCMR): https://github.com/henanjun/LCMR L. Fang, N. He, S. Li, A.J. Plaza, and Javier J. Plaza. A new spatial–spectral feature extraction method for hyperspectral images using local covariance matrix representation. IEEE Transactions on Geoscience and Remote Sensing, 56(6):3534–3546, 2018.
Laplacian-regularized low-rank subspace clustering (LLRSC): Please ask Professor Han Zhai for the implementation. H. Zhai, H. Zhang, L. Zhang, and P. Li. Laplacian-regularized low-rank subspace clustering for hyperspectral image band selection. IEEETransactions on Geoscience and Remote Sensing, 57(3):1723–1740,2019.
(c) Copyright Shukun Zhang, Tufts University, 2020.