This repository provides the implementations of EGCSC and EKGCSC model reported by "Y. Cai, Z. Zhang, Z. Cai, X. Liu, X. Jiang, and Q. Yan, “Graph convolutional subspace clustering: A robust subspace clustering framework for hyperspectral image,” IEEE Transactions on Geoscience and Remote Sensing, 2020"
If you would like to acknowledge our efforts, please cite the following paper:
@article{HSI-Clustering-GCSC-CAI-TGRS-2020,
title="Graph Convolutional Subspace Clustering: A Robust Subspace Clustering Framework for Hyperspectral Image",
author="Yaoming {Cai} and Zijia {Zhang} and Zhihua {Cai} and Xiaobo {Liu} and Xinwei {Jiang} and Qin {Yan}",
journal="IEEE Transactions on Geoscience and Remote Sensing",
note="doi: 10.1109/TGRS.2020.3018135",
year="2020",
}
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Python >= 3.5
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Numpy <= 1.16.2
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Munkres
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SciPy
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Scikit-Learn
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Spectral Python (SPy)
python demo.py
Dataset: HSI_Datasets/SalinasA_corrected.mat
original img shape: (83, 86, 204)
reduced img shape: (83, 86, 4)
x_patch tensor shape: (5348, 9, 9, 4)
final sample shape: (5348, 324), labels: [0. 1. 2. 3. 4. 5.]
============= EGCSC RESULTS =============
OA Kappa NMI 0.9993 0.9971 0.9991class accuracy: [1. 0.99702159 1. 1. 1. 1. ]
running time 42.296
============= EKGCSC RESULTS =============
OA Kappa NMI 1.0000 1.0000 1.0000class accuracy: [1. 1. 1. 1. 1. 1.]
running time 63.59
Reference hyper-parameter settings of EGCSC
===== =========== =========== ===========
data lambda K RO
===== =========== =========== ===========
SaA 10 30 0.8
InP 100 30 0.4 (13*13 patch)
PaU 1000 20 0.6
===========================================
Reference hyper-parameter settings of EKGCSC
===== =========== =========== =========== ==========
data lambda K RO gamma
===== =========== =========== =========== ==========
SaA 100 30 0.8 0.2
InP 1e3 30 0.8 10 (13*13 patch)
PaU 6*1e4 30 0.8 100
========================================================
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