Entropy-Guided Weighted Adversarial Open-Set Domain Adaptation Method for Hyperspectral Image Classification

This is a code demo for the paper "Entropy-Guided Weighted Adversarial Open-Set Domain Adaptation Method for Hyperspectral Image Classification"

Requirements

• Python Version: 3.8.20

• TorchMetrics Version: 1.5.1

• PyTorch Version: 1.12.0+cu113

• Scikit-learn Version: 1.3.2

• SciPy Version: 1.10.1

Datasets

Download dataset from the following link (code is qwer):BaiduYun, and move the files to folder./datasets .

An example datasets folder has the following structure:

datasets

├── PU-PC
│   ├── paviaU_gt_7.mat(source dataset)
│   ├── PaviaC_OS_gt.npy(target dataset)
├── HU13-HU18
│   ├── Houston13_7gt.mat(source dataset)
│   └── Houston18_7gt.mat(target dataset)

Usage

The pipeline for training with EGWA_OSDA is the following (The code is still being optimized):

1. Download the required dataset and move to folder./datasets.
2. run the script train.py