MATNet: Multilevel Attention-Based Transformers for Change Detection in Remote Sensing Images

This repo is the PyTorch implementation of some works related to remote sensing tasks.

💬 Network Architecture

Environment setting

Python 3.6.13
pytorch 1.9.1
torchvision 0.10.1+cpu

Please see requirements.txt for all the other requirements. You can create a virtual conda environment named ChangeFormer with the following cmd:

conda create --name MATNet --file requirements.txt
conda activate MATNet

Data structure

"""
Change detection data set with pixel-level binary labels；
├─A
├─B
├─label
└─list
"""

A: images of t1 phase;

B:images of t2 phase;

label: label maps;

list: contains train.txt, val.txt and test.txt, each file records the image names (XXX.png) in the change detection dataset.

You can download the processed LEVIR-CD and DSIFN-CD datasets by the DropBox through the following here:

• CDD:click here to download
• LEVIR-CD-256: click here to download
• DSIFN-CD-256: click here to download
• SYSU-CD: Baidu Netdisk, code: tgrs

💬 License

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

💬 Citation

If you use this code for your research, please cite our paper:

💬 References