SARAS-Net: Scale And Relation Aware Siamese Network for Change Detection

Target: Change detection aims to find the difference between two images at different times and output a change map.

This paper has been accepted in AAAI-23.

For more information, please see our paper at AAAI or arxiv.

Overview of SARAS-Net:

Visualize each module by Gradcam:

Requirements

cuda: 11.0  
python: 3.6.9  
pytorch: 1.7.0  
torchvision: 0.8.1 

Installation

git clone https://github.com/f64051041/SARAS-Net.git  
cd SARAS-Net  

Quick start

Download LEVIR-CD weight : https://drive.google.com/file/d/1Gs6iYQcZI1Jm4NhthCwWI2olbO-bpvTd/view?usp=share_link
After downloaded the model weight, you can put it in SARAS-Net/.
Then, run a demo to get started as follows:

python demo.py

After that, you can find the prediction results in SARAS-Net/samples/

Train

You can find SARAS-Net/cfgs/config.py to set the training parameter.

python train.py

Test

After training, you can put weight in SARAS-Net/.
Then, run a cal_acc.py to get started as follows:

python cal_acc.py

You can set show_result = True in cal_acc.py to show the result for each pairs.

Data structure

Train Data Path

train_dataset  
  |- train_dataset 
      |- image1, image2, gt  
  |- val_dataset  
      |- image1, image2, gt  
  |- train.txt
  |- val.txt

The format of train.txt and val.txt please refer to SARAS-Net/train_dataset/train.txt and SARAS-Net/train_dataset/val.txt

Test Data Path

test_dataset  
  |- A 
      |- image1 
  |- B  
      |- image2 
  |- label
      |- gt 

Data Download

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

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

DSIFN-CD: https://github.com/GeoZcx/A-deeply-supervised-image-fusion-network-for-change-detection-in-remote-sensing-images/tree/master/dataset

Quick train on LEVIR-CD : https://drive.google.com/file/d/1DAlxuqalNIPopt-WgtDmCYO98_jWM3ER/view?usp=share_link

Quick test on LEVIR-CD : https://drive.google.com/file/d/1Bj5GQ3hZcDVSpFGZKxm7zIuCBP5XEr6x/view?usp=share_link

Result

Dataset	Pre.	Rec.	F1-score	IoU	OA
LEVIR-CD	91.97%	91.85%	91.91%	84.95%	99.10%
CCD-CD	97.76%	97.23%	97.49%	95.11%	99.35%
WHU-CD	88.41%	85.81%	87.09%	77.14%	98.89%
DSIFN-CD	67.65%	67.51%	67.58%	51.04%	89.01%