DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral Imagery, TCSVT, 2022
Bobo Xi, Jiaojiao Li, Yan Diao, Yunsong Li, Zan Li, Yan Huang, and Jocelyn Chanussot.
Code for the paper: DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral Imagery.
Fig. 1: Architecture of the proposed DGSSC for imbalanced hyperspectral imagery. The encoder, decoder, and classifier are trained in an end-to-end fashion, driven by the integrated loss of LPdRec, Lmmd, and Lcls.Please run the 'main.py' to reproduce the DGSSC results on Lokia dataset.
We have successfully tested it on Ubuntu 16.04 with Tensorflow 1.13.1. Below is the classification result when 1% labeled samples are selected as the training set.
If you find this code helpful, please kindly cite:
[1] B. Xi, J. Li, Y. Diao, Y. Li, Z. Li, Y. Huang, and J. Chanussot, "DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral Imagery," in IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1, 2022, doi: 10.1109/TCSVT.2022.3215513.
BibTeX entry:
@ARTICLE{Xi2022_TCSVT_DGSSC,
author={Xi, Bobo and Li, Jiaojiao and Diao, Yan and Li, Yunsong and Li, Zan and Huang, Yan and Chanussot, Jocelyn},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
title={DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral Imagery},
year={2022},
volume={},
number={},
pages={1-1},
doi={10.1109/TCSVT.2022.3215513}}
Copyright (C) 2022 Bobo Xi
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