Robust feature aggregation network for lightweight and effective remote sensing image change detection (ISPRS 2024)

Authors: Zhi-Hui You; Si-Bao Chen; Jia-Xin Wang; Bin Luo

This repository contains simple pytorch implementation of our paper RFANet.

1. Overview

A lightweight change detection network, called as robust feature aggregation network (RFANet). To improve representative capability of weaker features extracted from lightweight backbone, a feature reinforcement module (FRM) is proposed. FRM allows current level feature to densely interact and fuse with other level features, thus accomplishing the complementarity of fine-grained details and semantic information. Considering massive objects with rich correlations in RS images, we design semantic split-aggregation module (SSAM) to better capture global semantic information of changed objects. Besides, we present a lightweight decoder containing channel interaction module (CIM), which allows multi-level refined difference features to emphasize changed areas and suppress background and pseudo-changes.

2. Usage

2.1 Dataset

• Prepare the data: Download the change detection datasets from the following links. Place them inside your datasets folder.

  • LEVIR-CD
  • WHU-CD
  • CDD-CD
  • SYSU-CD
  • ./samples/test is a sample to start quickly.

• Crop all datasets into 256x256 patches.
• Generate list file as ls -R ./label/* > test.txt
• Prepare datasets into following structure and set their path in train.py and test.py

  ├─A
      ├─A1.jpg/png
      ├─A2.jpg/png
      ├─...jpg/png
      └─...jpg/png
  ├─B
      ├─B1.jpg/png
      ├─B2.jpg/png
      ├─...jpg/png
      └─...jpg/png
  ├─label
      ├─label1.jpg/png
      ├─label2.jpg/png
      ├─...jpg/png
      └─...jpg/png
  ├─list
      ├─train.txt
      ├─val.txt
      └─test.txt
  

2.2 Setting up conda environment

• Prerequisites for Python:
  • Creating a virtual environment in terminal: conda create -n RFANet python=3.8
  • Installing necessary packages: pip install -r requirements.txt

2.3 Installation

• Clone this repo:

  git clone https://github.com/Youzhihui/RFANet.git
  cd RFANet

2.4 Quick Start

• Train/Test
  • sh ./train_test_tools/train.sh
  • sh ./train_test_tools/test.sh

3. Change Detection Results

The quantitative results of different CD methods on LEVIR-CD, WHU-CD, and CDD-CD. The last column indicates the mean value of same report on different datasets. Color convention: best (red-bold), 2nd-best (blue-underline), and 3nd-best (orange-italic).

Heatmap visualizations of multiple levels for RFANet.

4. Acknowlogdement

This repository is built under the help of the projects A2Net, BIT_CD, CDLab, and MobileSal for academic use only.

5. Citation

Please cite our paper if you find the work useful:

@article{you2024robust,
    title={Robust feature aggregation network for lightweight and effective remote sensing image change detection},
    author={You, Zhi-Hui and Chen, Si-Bao and Wang, Jia-Xin and Luo, Bin},
    journal={ISPRS Journal of Photogrammetry and Remote Sensing},
    volume={215},
    pages={31--43},
    year={2024},
    publisher={Elsevier}
    }