jackliu-97 / misgnet

MISGNet: Multilevel Intertemporal Semantic Guidance Network for Remote Sensing Images Change Detection

Here, we provide the pytorch implementation of the paper: MISGNet: Multilevel Intertemporal Semantic Guidance Network for Remote Sensing Images Change Detection.

Overall Architecture

Semantics Guidance Module (SGM)

Multilevel Difference Aggregation Module

Requirements

albumentations>=1.3.0
numpy>=1.20.2
opencv_python>=4.7.0.72
opencv_python_headless>=4.7.0.72
Pillow>=9.4.0
Pillow>=9.5.0
scikit_learn>=1.0.2
torch>=1.9.0
torchvision>=0.10.0

Installation

Clone this repo:

git clone https://github.com/JackLiu-97/MISGNet.git
cd MISGNet

Quick Start

Firstly, you can download our MISGNet pretrained model

LEVIR-CD： baidu drive, code: itrs .

SYSU-CD： baidu drive, code: itrs .

After downloaded the pretrained model, you can put it in output.

Then, run a demo to get started as follows:

python demo.py --ckpt_url ${model_path} --data_path ${sample_data_path}  --out_path ${out_data_path} 

Train

To train a model from scratch, use

python train.py --data_path ${train_data_path} --val_path ${val_data_path} --lr ${lr} --batch_size ${-batch_size} 

Evaluate

To evaluate a model on the test subset, use

python predict.py --ckpt_url ${model_path} --data_path ${test_data_path}

Result

In order to make it more convenient for readers to compare with our model, we also provide the inference results of our model.

LEVIR-CD： baidu drive, code: itrs .

SYSU-CD： baidu drive, code: itrs .

Supported Datasets

WHU-CD :The WHU Building Change Detection Dataset :The dataconsists of two aerial images of two different time phases and the exact location, which contains $12796$ buildings in $20.5km^2$ with a resolution of $0.2 m$ and a size of $32570\times15354$.We crop the images to $256\times256$ size and randomly divide the training, validation, and test sets:$ 6096/762/762$. LEVIR-CD : The dataset consists of $637$ very high-resolution (VHR, $0.5$m/pixel) Google Earth image patch pairs with a size of $1024 \times 1024$ pixels. These bitemporal images with time span of $5$ to $14$ years have significant land-use changes, especially the construction growth. LEVIR-CD covers various types of buildings, such as villa residences, tall apartments, small garages and large warehouses. The fully annotated LEVIR-CD contains a total of $31,333$ individual change building instances.

SYSU-CD : The dataset contains $20000$ pairs of $0.5$-m aerial images of size $256 \times 256$ taken between the years $2007$ and $2014$ in Hong Kong. The main types of changes in the dataset include: $(a)$ newly built urban buildings; $(b)$ suburban dilation; $ (c)$ groundwork before construction; $(d)$ change of vegetation; $(e)$ road expansion; $(f)$ sea construction.

Dataset	Name	Link
LEVIR-CD building change detection dataset	LEVIR-CD	website
SYSU-CD building change detection dataset	SYSU-CD	website
WHU building change detection dataset	WHU-CD	website

License

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