DCENet (Diff-Feature Contrast Enhancement Network)

Pytorch implementation for DCENet 《DCENet: Diff-Feature Contrast Enhancement Network for Semi-supervised Hyperspectral Change Detection》

F. Luo, T. Zhou, J. Liu, T. Guo, X. Gong and X. Gao, "DCENet: Diff-Feature Contrast Enhancement Network for Semi-supervised Hyperspectral Change Detection," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2024.3374600. 

Introduction

This repository includes DCENet implementations in PyTorch version and partial datasets in the paper.

Requirements

Python 3.9
PyTorch 1.10.2

Citation

Please cite our paper if you use this code in your research.

F. Luo, T. Zhou, J. Liu, T. Guo, X. Gong and X. Gao, "DCENet: Diff-Feature Contrast Enhancement Network for Semi-Supervised Hyperspectral Change Detection," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-14, 2024, Art no. 5511514, doi: 10.1109/TGRS.2024.3374600.

@ARTICLE{10462227,
  author={Luo, Fulin and Zhou, Tianyuan and Liu, Jiamin and Guo, Tan and Gong, Xiuwen and Gao, Xinbo},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={DCENet: Diff-Feature Contrast Enhancement Network for Semi-Supervised Hyperspectral Change Detection}, 
  year={2024},
  volume={62},
  number={},
  pages={1-14},
  keywords={Feature extraction;Training;Data mining;Probabilistic logic;Hyperspectral imaging;Decoding;Data models;Change detection (CD);contrastive learning;hyperspectral image;multiscale feature;Siamese network},
  doi={10.1109/TGRS.2024.3374600}}

License

Code and datasets are released for non-commercial and research purposes only. For commercial purposes, please contact the authors.