Precision Spatio-Temporal Feature Fusion for Robust Remote Sensing Change Detection

Welcome to our cutting-edge implementation for remote sensing change detection! This project enhances the ChangeMamba architecture with precision fusion blocks, an enhanced decoder pipeline, and an improved optimization strategy, delivering unparalleled accuracy in detecting spatio-temporal changes.

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🔥🔥 Updates

• Our paper is live at IEEE Xplorer

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🚀 Introduction

Monitoring changes in remote sensing imagery is vital for tracking environmental and urban dynamics. Our approach excels with:

• 🔍 Precision Fusion Blocks: Detect subtle temporal shifts using channel-wise cross modeling and explicit difference modules.
• 🧠 Enhanced Decoder Pipeline: Preserve fine details efficiently with lightweight convolutions and CBAM.
• ⚙️ Optimized Learning: Tackle class imbalance and boost IoU with a blend of Dice loss, cross-entropy, and Lovász objectives.

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🛠️ Installation

Set up the project in a few simple steps:

1. Clone the repository:

   git clone https://github.com/Buddhi19/MambaCD.git
   cd MambaCD

2. Create a Conda environment:

   conda create -n mamba_cd
   conda activate mamba_cd

3. Install dependencies:

   • Install PyTorch following the official instructions for your system.
   • Install remaining dependencies:

     pip install -r requirements.txt

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🎯 Quick Start

🏋️ Training

Kick off training with:

python train.py

🔍 Inference

Refer to annotation/Ours.ipynb for detailed inference steps.

📊 Results

Our model excels on datasets like LEVIR-CD+, SYSU-CD, and WHU-CD. See the qualitative results:

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📥 Pretrained Models

Download our pretrained models below:

Dataset	IoU (%)	Download Link
LEVIR-CD+	83.32	Link
SYSU-CD	75.04	Link
WHU-CD	89.95	Link

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🧩 Key Features

Discover how our innovations elevate change detection:

Caption: Showcasing precision fusion blocks, enhanced decoder, and STSS block enhancements.

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If you find our work helpful please cite

@INPROCEEDINGS{11450773,
  author={Wijenayake, W.M.B.S.K. and Ratnayake, R.M.A.M.B. and Sumanasekara, D.M.U.P. and Wasalathilaka, N.S. and Piratheepan, M. and Godaliyadda, G.M.R.I. and Ekanayake, M.P.B. and Herath, H.M.V.R.},
  booktitle={2025 IEEE 19th International Conference on Industrial and Information Systems (ICIIS)}, 
  title={Precision Spatio-Temporal Feature Fusion for Robust Remote Sensing Change Detection}, 
  year={2026},
  volume={19},
  number={},
  pages={557-562},
  keywords={Accuracy;Computational modeling;Pipelines;Feature extraction;Transformers;Decoding;Remote sensing;Optimization;Monitoring;Context modeling;Remote Sensing;Binary Change Detection;State Space Models;Mamba},
  doi={10.1109/ICIIS69028.2026.11450773}}

You may also cite the experimental paper that confirms improvements caused by CBAM

@INPROCEEDINGS{11217111,
  author={Ratnayake, R.M.A.M.B. and Wijenayake, W.M.B.S.K. and Sumanasekara, D.M.U.P. and Godaliyadda, G.M.R.I. and Herath, H.M.V.R. and Ekanayake, M.P.B.},
  booktitle={2025 Moratuwa Engineering Research Conference (MERCon)}, 
  title={Enhanced SCanNet with CBAM and Dice Loss for Semantic Change Detection}, 
  year={2025},
  volume={},
  number={},
  pages={84-89},
  keywords={Training;Accuracy;Attention mechanisms;Sensitivity;Semantics;Refining;Feature extraction;Transformers;Power capacitors;Remote sensing},
  doi={10.1109/MERCon67903.2025.11217111}}

🙏 Acknowledgments

A heartfelt thank you to ChenHongruixuan/ChangeMamba for the foundational code that sparked this project!

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