README.md

November 27, 2025 · View on GitHub

BiFA: Remote Sensing Image Change Detection with Bitemporal Feature Alignment

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Introduction

This repository is the code implementation of the paper BiFA: Remote Sensing Image Change Detection with Bitemporal Feature Alignment

The current branch has been tested on Linux system, PyTorch 1.12.0 and CUDA 12.1, supports Python 3.7.

If you find this project helpful, please give us a star ⭐️, your support is our greatest motivation.

Updates

🌟 2024.06.20 Revised the BiFA project.

TODO

Updated more change detection methods

Introduction
Benchmark
TODO
Table of Contents
Installation
Dataset Preparation
Model Training and Testing
Citation
License
Contact Us

Installation

Requirements

Linux system, Windows, depending on MMCV can be installed
Python 3.6+, recommended 3.7
PyTorch 1.10+ or higher, recommended 1.12.0
CUDA 11.7 or higher, recommended 12.1

It is recommended to use Miniconda for installation. The following commands will create a virtual environment named bifa and install PyTorch. In the following installation steps, the default installed CUDA version is 12.1. If your CUDA version is not 12.1, please modify it according to the actual situation.

Note: If you are experienced with PyTorch and have already installed it, you can skip to the next section. Otherwise, you can follow the steps below.

Step 0: Install Miniconda.

Step 1: Create a virtual environment named bifa and activate it.

conda create -n bifa python=3.7
conda activate bifa

Step 2: Install dependencies.

pip install -r requirements.txt

Note: Use the following command to install mmcv

pip install mmcv-full==1.6.2 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.12/index.html

Install BiFA

You can download or clone the BiFA repository.

git clone git@github.com:zmoka-zht/BiFA.git
cd BiFA

Dataset Preparation

Remote Sensing Change Detection Dataset

We provide the method of preparing the remote sensing change detection dataset used in the paper.

WHU-CD Dataset

Data download link: WHU-CD Dataset PanBaiDu. Code:t2sb

LEVIR-CD Dataset

Data download link: LEVIR-CD Dataset PanBaiDu. Code:5ss2

LEVIR+-CD Dataset

Data download link: LEVIR+-CD Dataset PanBaiDu. Code: xtj8

SYSU-CD Dataset

Data download link: SYSU-CD Dataset PanBaiDu. Code: gk0w

CLCD-CD Dataset

Data download link: CLCD-CD Dataset PanBaiDu. Code: bcml

DSIFN-CD Dataset

Data download link: DSIFN-CD Dataset PanBaiDu. Code: u8e9

Organization Method

You can also choose other sources to download the data, but you need to organize the dataset in the following format：

${DATASET_ROOT} # Dataset root directory, for example: /home/username/data/LEVIR-CD
├── A
│   ├── train_1_1.png
│   ├── train_1_2.png
│   ├──...
│   ├── val_1_1.png
│   ├── val_1_2.png
│   ├──...
│   ├── test_1_1.png
│   ├── test_1_2.png
│   └── ...
├── B
│   ├── train_1_1.png
│   ├── train_1_2.png
│   ├──...
│   ├── val_1_1.png
│   ├── val_1_2.png
│   ├──...
│   ├── test_1_1.png
│   ├── test_1_2.png
│   └── ...
├── label
│   ├── train_1_1.png
│   ├── train_1_2.png
│   ├──...
│   ├── val_1_1.png
│   ├── val_1_2.png
│   ├──...
│   ├── test_1_1.png
│   ├── test_1_2.png
│   └── ...
├── list
│   ├── train.txt
│   ├── val.txt
│   └── test.txt

Model Training and Testing

All configuration for model training and testing are stored in the local folder config

Example of Training on LEVIR-CD Dataset

python train.py --config/levir.json

Example of Testing on LEVIR-CD Dataset

python test.py --config/levir_test_bifa.json

BiFA Weight

The model weights of BiFA are provided in the experiments/pretrain
Segformer weight download link: Segformer PanBaiDu. Code:i81p

Citation

If you use the code or performance benchmarks of this project in your research, please refer to the following bibtex citation of BiFA.

@ARTICLE{10471555,
  author={Zhang, Haotian and Chen, Hao and Zhou, Chenyao and Chen, Keyan and Liu, Chenyang and Zou, Zhengxia and Shi, Zhenwei},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={BiFA: Remote Sensing Image Change Detection With Bitemporal Feature Alignment}, 
  year={2024},
  volume={62},
  number={},
  pages={1-17},
  keywords={Feature extraction;Task analysis;Remote sensing;Transformers;Interference;Decoding;Optical flow;Bitemporal interaction (BI);change detection (CD);feature alignment;flow field;high-resolution optical remote sensing image;implicit neural representation},
  doi={10.1109/TGRS.2024.3376673}}

@ARTICLE{11268372,
  author={Zhang, Haotian and Guo, Han and Chen, Keyan and Chen, Hao and Zou, Zhengxia and Shi, Zhenwei},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={FoBa: A Foreground-Background co-Guided Method and New Benchmark for Remote Sensing Semantic Change Detection}, 
  year={2025},
  volume={},
  number={},
  pages={1-1},
  keywords={Semantics;Remote sensing;Transformers;Feature extraction;Annotations;Roads;Multitasking;Spatial resolution;Landsat;Land surface;Semantic change detection (SCD);foreground-background co-guided;bi-temporal interaction;mamba;new benchmark},
  doi={10.1109/TGRS.2025.3636947}}

@ARTICLE{10902569,
  author={Zhang, Haotian and Chen, Keyan and Liu, Chenyang and Chen, Hao and Zou, Zhengxia and Shi, Zhenwei},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={CDMamba: Incorporating Local Clues Into Mamba for Remote Sensing Image Binary Change Detection}, 
  year={2025},
  volume={63},
  number={},
  pages={1-16},
  keywords={Feature extraction;Transformers;Remote sensing;Convolutional neural networks;Visualization;Artificial intelligence;Spatiotemporal phenomena;Computational modeling;Attention mechanisms;Computer vision;Bi-temporal interaction;change detection (CD);high-resolution optical remote sensing image;Mamba;state-space model},
  doi={10.1109/TGRS.2025.3545012}}

License

This project is licensed under the Apache 2.0 License.

Contact Us

If you have any other questions❓, please contact us in time 👬