https://github.com/wm-Githuber/AFCF3D-Net

https://github.com/wm-Githuber/AFCF3D-Net

Last synced: about 2 months ago
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• Host: GitHub
• URL: https://github.com/wm-Githuber/AFCF3D-Net
• Owner: wm-Githuber
• Created: 2022-12-19T11:58:39.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-02-16T01:58:40.000Z (7 months ago)
• Last Synced: 2024-04-19T23:12:11.671Z (5 months ago)
• Language: Python
• Size: 3.46 MB
• Stars: 11
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-remote-sensing-change-detection - Ye Y, Wang M, Zhou L, et al. Adjacent-level feature cross-fusion with 3D CNN for remote sensing image change detection

README

        
# Adjacent-Level Feature Cross-Fusion with 3-D CNN for Remote Sensing Image Change Detection

Here, we provide the official pytorch implementation of the paper "Adjacent-Level Feature Cross-Fusion with 3-D CNN for Remote Sensing Image Change Detection".

![Architecture](https://github.com/wm-Githuber/AFCF3D-Net/assets/66511993/9c2681a4-a582-4b73-8133-55f2c5da5dc9)

# Requirements

* python        3.9.12

* numpy         1.23.1

* pytorch       1.12.1

* torchvision   0.13.1

# Dataset Preparation

## Data Structure

"""  

Change detection data set with pixel-level binary labels;  

├─A  

├─B  

├─label  

└─list  

  ├─train.txt  

  ├─val.txt  

  ├─test.txt  

"""  

A: Images of T1 time  

B: Images of T2 time  

label: label maps  

list: contrains train.txt, val.txt, and test.txt. each fild records the name of image paris (XXX.png).  

## Data Download  

WHU-CD: https://study.rsgis.whu.edu.cn/pages/download/building_dataset.html  

LEVIR-CD: https://justchenhao.github.io/LEVIR/  

SYSU-CD: https://github.com/liumency/SYSU-CD  

# Training and Testing

train.py  

Test.py

# Quantitative Results

![image](https://github.com/wm-Githuber/AFCF3D-Net/assets/66511993/7612d847-8ccb-422d-9fee-3b567b8082a4)

# Qualitative Results

![SYSU-result](https://user-images.githubusercontent.com/66511993/210714033-e006d556-97d1-47e9-8423-3de7a983f385.png)

# Licence

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

# Citation

If you find this work interesting in your research, please cite our paper as follow:  

@ARTICLE{YeCD,  

  author={Ye, Yuanxin and Wang, Mengmeng and Zhou, Liang and Lei, Guangyang and Fan, Jianwei and Qin, Yao},  

         journal={IEEE Transactions on Geoscience and Remote Sensing},  

         title={Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection},  

         year={2023},  

         volume={61},  

         number={},  

         pages={1-14},  

         doi={10.1109/TGRS.2023.3305499}}