January 2022 IEEE Transactions on Geoscience and Remote Sensing 60:1-1 Follow journal DOI: 10.1109/TGRS.2022.3166817 This is a code demo for the paper "Confident Learning-Based Domain Adaptation for Hyperspectral Image Classification"
Some of our code references the projects
CUDA = 10.2
Python = 3.7
Pytorch = 1.5
sklearn = 0.23.2
numpy = 1.19.2
cleanlab = 1.0
You can download the hyperspectral datasets in mat format at:https://pan.baidu.com/s/184BXDD2KnlreqXX70Nar4Q?pwd=kfgj, and move the files to ./datasets folder.
An example dataset folder has the following structure:
datasets
├──Indiana
│ ├── DataCube.mat
├── Houston
│ ├── Houston13.mat
│ └── Houston13_7gt.mat
│ ├── Houston18.mat
│ └── Houston18_7gt.mat
├── Pavia
│ ├── paviaU.mat
│ └── paviaU_gt_7.mat
│ ├── pavia.mat
│ └── pavia_gt_7.mat
│── Shanghai-Hangzhou
│ └── DataCube.mat
Take CLDA method on the UP2PC dataset as an example:
- Open a terminal or put it into a pycharm project.
- Put the dataset into the correct path.
- Run CLDA_UP2PC.py. `
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