README.md

Towards Accurate and Reliable Change Detection of Remote Sensing Images via Knowledge Review and Online Uncertainty Estimation (Under Review)

This repository contains simple python implementation of our paper AR-CDNet.

1. Overview

A framework of the proposed AR-CDNet. Initially, the bi-temporal images pass through a shared feature extractor to obtain bi-temporal features, and then multi-level temporal difference features are obtained through the TDE. The OUE branch estimates pixel-wise uncertainty supervised by the diversity between predicted change maps and corresponding ground truth in the training process. KRMs fully explore the multi-level temporal difference knowledge. Finally, the multi-level temporal difference features and uncertainty-aware features obtained from the OUE branch are aggregated to generate the final change maps.

2. Usage

• Prepare the data:

  • Download datasets LEVIR, and BCDD.
  • Crop LEVIR and BCDD datasets into 512x512 patches. The pre-processed LEVIR and BCDD datasets can be obtained from BCDD_512x512, BCDD_512x512.
  • Generate list file as ls -R ./label/* > test.txt
  • Prepare datasets into the following structure and set their path in train.py and test.py

  ├─Train
      ├─A        ...jpg/png
      ├─B        ...jpg/png
      ├─label    ...jpg/png
      └─list     ...txt
  ├─Val
      ├─A
      ├─B
      ├─label
      └─list
  ├─Test
      ├─A
      ├─B
      ├─label
      └─list
  

• Prerequisites for Python:

  • Creating a virtual environment in the terminal: conda create -n AR-CDNet python=3.8
  • Installing necessary packages: pip install -r requirements.txt

• Train/Test

  • sh train.sh
  • sh test.sh

3. Citation

Please cite our paper if you find the work useful:

@article{Li_2023_MSL-MKC,
        title={Towards Accurate and Reliable Change Detection of Remote Sensing Images via Knowledge Review and Online Uncertainty Estimation},
        author={Li, Zhenglai and Tang, Chang and Li, Xianju and Xie, Weiying and Sun, Kun and Zhu, Xinzhong},
        journal={arXiv preprint arXiv:2305.19513},
        year={2023}
    }