Mind the Gap😮: Mutil-Level Unsupervised Domain Adaptation for Cross-scene Hyperspectral Image Classification, TGRS, 2024
Mingshuo Cai,Bobo Xi, Jiaojiao Li,Shou Feng, Yunsong Li, Zan Li, and Jocelyn Chanussot.
Code for the paper: Mind the Gap: Mutil-Level Unsupervised Domain Adaptation for Cross-scene Hyperspectral Image Classification.🤩
Fig. 1. The flowchart of the MLUDA, illustrating the domain adaptation strategies at the image-, feature-, and logic-level.
Houtson 18
Pavia City
Hangzhou
- You can download the dataset in my google drive!😲
Now you can find more information in our page MLUDA | Project page (cfcys.github.io)!🥳
If you find this code helpful😊, please kindly cite:
M. Cai et al., "Mind the Gap: Multi-Level Unsupervised Domain Adaptation for Cross-scene Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2024.3407952. keywords: {Feature extraction;Image color analysis;Convolutional neural networks;Training;Task analysis;Visualization;Hyperspectral imaging;Cross-scene;domain adaptation;guided filter;cross attention;supervised contrastive learning},
or you can give me a little⭐!
BibTeX entry:
@ARTICLE{10543066,
author={Cai, Mingshuo and Xi, Bobo and Li, Jiaojiao and Feng, Shou and Li, Yunsong and Li, Zan and Chanussot, Jocelyn},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Mind the Gap: Multi-Level Unsupervised Domain Adaptation for Cross-scene Hyperspectral Image Classification},
year={2024},
volume={},
number={},
pages={1-1},
keywords={Feature extraction;Image color analysis;Convolutional neural networks;Training;Task analysis;Visualization;Hyperspectral imaging;Cross-scene;domain adaptation;guided filter;cross attention;supervised contrastive learning},
doi={10.1109/TGRS.2024.3407952}}
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