MADANet: A Lightweight Hyperspectral Image Classification Network

Overview

This is a project about MADANet, a lightweight hyperspectral image classification network that combines multi-scale feature aggregation and dual attention mechanism. The goal of this project is to implement this network and test it on various hyperspectral image datasets.

Article Citation

Cui, B.; Wen, J.; Song, X.; He, J. MADANet: A Lightweight Hyperspectral Image Classification Network with Multi-Scale Feature Aggregation and Dual Attention Mechanism. Remote Sens. 2023, 15, 5222. https://doi.org/10.3390/rs15215222

Project Structure

• .idea/: Folder for IDE settings
• .gitignore: Lists files and folders to be ignored by git
• CBAM.py: Contains code for Convolutional Block Attention Module (CBAM)
• DANet.py: Contains code for Dual Attention Network (DANet)
• Indian_pines_corrected.mat: File for the Indian Pines dataset
• Indian_pines_gt.mat: File for the ground truth labels of the Indian Pines dataset
• LICENSE: Project license file
• MADANet.py: Contains code for the MADANet network
• dataset: Contains code for handling the dataset
• evaluate.py: Contains code for evaluating the model performance
• train.py: Contains code for training the model

How to Run

1. Clone this repository
2. Download and prepare your datasets
3. Run train.py

Results

Our model has only 0.16 M model parameters on the Indian Pines (IP) dataset, but the overall accuracy is as high as 98.34%. Similarly, the framework achieved overall accuracies of 99.13%, 99.17%, and 99.08% on the University of Pavia (PU), Salinas (SA), and WHU Hi LongKou (LongKou) datasets, respectively.

Contributions

If you have any suggestions or issues for this project, feel free to raise an issue or pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.