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 or arxiv.

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:

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:

#!/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

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}
}