Awesome
Introduction
The Pytorch implementation for: "LightCDNet: Lightweight Change Detection Network Based on VHR Images" (IEEE GRSL' 2023)
You can get a PDF version of our paper here: Google Drive
Abstract
| Method | Crop Size | Lr schd | #Param (M) | MACs (G) | Precision | Recall | F1-Score | IoU |
|---|---|---|---|---|---|---|---|---|
| LightCDNet-small | 256x256 | 40000 | 0.35 | 1.65 | 91.36 | 89.81 | 90.57 | 82.77 |
| LightCDNet-base | 256x256 | 40000 | 1.32 | 3.22 | 92.12 | 90.43 | 91.27 | 83.94 |
| LightCDNet-large | 256x256 | 40000 | 2.82 | 5.94 | 92.43 | 90.45 | 91.43 | 84.21 |
Notice
The code has been integrated into Open-CD, welcome to use it!
Environment installation
This project is implemented based on Open-CD, please refer to the installation method of Open-CD 0.x version.
Usage
(eg: LightCDNet-small)
Train:
python tools/train.py configs/lightcdnet/lightcdnet_s_256x256_40k_levircd.py --work-dir ./exp/lightcdnet_s_levir_workdir --gpu-id 0 --seed 602
Eval:
python tools/test.py configs/lightcdnet/lightcdnet_s_256x256_40k_levircd.py ./exp/lightcdnet_s_levir_workdir/latest.pth --eval mFscore mIoU
Visualization:
python tools/test.py configs/lightcdnet/lightcdnet_s_256x256_40k_levircd.py ./exp/lightcdnet_s_levir_workdir/latest.pth --format-only --eval-options "imgfile_prefix=tmp_infer"
Citation
If you find this project useful in your research, please consider cite:
@ARTICLE{10214556,
author={Xing, Yuanjun and Jiang, Jiawei and Xiang, Jun and Yan, Enping and Song, Yabin and Mo, Dengkui},
journal={IEEE Geoscience and Remote Sensing Letters},
title={LightCDNet: Lightweight Change Detection Network Based on VHR Images},
year={2023},
volume={20},
number={},
pages={1-5},
doi={10.1109/LGRS.2023.3304309}}