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https://github.com/justchenhao/SaDL_CD
Semantic-Aware Dense Representation Learning for Remote Sensing Image Change Detection
https://github.com/justchenhao/SaDL_CD
change-detection pretraining representation-learning
Last synced: 3 months ago
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Semantic-Aware Dense Representation Learning for Remote Sensing Image Change Detection
- Host: GitHub
- URL: https://github.com/justchenhao/SaDL_CD
- Owner: justchenhao
- License: bsd-2-clause
- Created: 2022-09-10T08:38:15.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-04-07T04:52:20.000Z (over 1 year ago)
- Last Synced: 2024-07-23T03:38:45.449Z (4 months ago)
- Topics: change-detection, pretraining, representation-learning
- Language: Python
- Homepage:
- Size: 1.96 MB
- Stars: 33
- Watchers: 2
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-remote-sensing-change-detection - Chen H, Li W, Chen S, et al. Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection
README
# SaDL_CD
Here, we provide the pytorch implementation of the paper: Semantic-Aware Dense Representation Learning for Remote Sensing Image Change Detection.
For more ore information, please see our published paper at [IEEE TGRS](https://ieeexplore.ieee.org/document/9874899/) or [arxiv](https://arxiv.org/abs/2205.13769).
## Requirements
```
Python 3.7
pytorch 1.10.1
torchvision 0.11.2
einops 0.3.0
kornia 0.6.3
```
## logs
20230407： update the download link of the cropped Inria data.
## Installation
Clone this repo:
```shell
git clone https://github.com/justchenhao/SaDL_CD.git
cd SaDL_CD
```
## Quick Start
You can simply run `python main_ssl.py` to train our model (`sadl_fpn_m2_resnet18_sample16_syn1`) on the given small samples (in the folder `samples`).
## Training
You can find the training script `train_ssl.sh` in the folder `scripts`. You can run the script file by `sh scripts/train_ssl.sh` in the command environment.
The detailed script file `train_ssl.sh` is as follows:
```shell
#!/usr/bin/env bash
gpus=0
checkpoint_root=checkpoints
dataset_type=SegDataset
img_size=256
batch_size=64
optim_mode=sgd
lr_policy=poly
lr=0.01
max_epochs=200
net_G=sadl_fpn_m2_resnet18_sample16_syn1
data_name=inria256
split=pos0.1_train
split_val=pos0.1_val
project_name=SSLM_${net_G}_${data_name}_b${batch_size}_lr${lr}_${split}_${split_val}_${max_epochs}_${lr_policy}_${optim_mode}
python main_ssl.py --dataset_type ${dataset_type} --img_size ${img_size} --optim_mode ${optim_mode} --checkpoint_root ${checkpoint_root} --lr_policy ${lr_policy} --split ${split} --split_val ${split_val} --net_G ${net_G} --gpu_ids ${gpus} --max_epochs ${max_epochs} --project_name ${project_name} --batch_size ${batch_size} --data_name ${data_name} --lr ${lr}
```
## Dataset Preparation
### Pretraining dataset
We leverage image-mask pairs from the existing Inria building segmentation dataset. We cut the original samples into small patches of size 256 × 256. We additionally obtain the coregistered image patch of the corresponding geospatial region.
The original Inria building segmentation dataset can be found at: https://project.inria.fr/aerialimagelabeling/
Our processed pretraining dataset can be accessed by Baidu yun (code: 2p1b): [link](https://pan.baidu.com/s/1ObAu_4Xm3SWCdMvAfiN7yw)
Note that you need only the image-mask pairs in `A` and `label` to train our model. We also provide the spatially registered image of another temporal in `B` for possible usage.
#### Data structure
```
"""
The pretraining data set with bitemporal images and building mask for one temporal. Note that the masks in the folder 'label' are aligned with the corresponding images in folder 'A'；
├─A
├─B
├─label
└─list
"""
```
#### Data Download
### Downstream Datasets
We test our pretrained model at three downstream change detection datasets.
#### Data structure
```
"""
Change detection data set with pixel-level binary labels；
├─A
├─B
├─label
└─list
"""
```
#### Data Download
LEVIR-CD: https://justchenhao.github.io/LEVIR/
WHU-CD: https://study.rsgis.whu.edu.cn/pages/download/building_dataset.html
GZ-CD: https://github.com/daifeng2016/Change-Detection-Dataset-for-High-Resolution-Satellite-Imagery
## 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:
```
@Article{chen2022,
title={Semantic-Aware Dense Representation Learning for Remote Sensing Image Change Detection},
author={Hao Chen, Wenyuan Li, Song Chen and Zhenwei Shi},
year={2022},
journal={IEEE Transactions on Geoscience and Remote Sensing},
volume={},
number={},
pages={1-18},
doi={10.1109/TGRS.2022.3203769}
}
```