🚀 Prototype-Based Information Compensation Network for Multi-Source Remote Sensing Data Classification, IEEE TGRS 2025

📌 Introduction

This repository contains the official implementation of our paper:
📄 Prototype-Based Information Compensation Network for Multi-Source Remote Sensing Data Classification, IEEE TGRS 2025

Prototype-based Information Compensation Network ( PICNet ) is designed for land cover classification based on HSI and SAR/LiDAR data. Specifically, we first design a frequency interaction module to enhance the inter-frequency coupling in multi-source feature extraction. The multi-source features are first decoupled into high- and low-frequency components. Then, these features are recoupled to achieve efficient inter-frequency communication. Afterward, we design a prototype-based information compensation module to model the global multi-source complementary information. Two sets of learnable modality prototypes are introduced to represent the global modality information of multi-source data.

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🔍 Key Features

🔍 Key Features:
✅ Cross-Modal Remote Sensing Data Joint Classification
✅ Frequency interaction module
✅ Prototype-based information compensation module

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📂 Dataset

The dataset used in our experiments can be accessed from the following link:
📥 Download Dataset Berlin and Augsburg

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🏋️‍♂️ Usage: Training PICNet

To train PICNet , use the following command:

python task.py

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📬 Contact

If you have any questions, feel free to contact us via Email:
📧 Feng Gao: gaofeng@ouc.edu.cn
📧 Sheng Liu: ls1290751536@163.com 📧 Chuanzheng Gong: gongchuanzheng@stu.ouc.edu.com

We hope PICNet helps your research! ⭐ If you find our work useful, please cite:

@ARTICLE{picnet2025,
  author={Gao, Feng and Liu, Sheng and Gong, Chuanzheng and Zhou, Xiaowei and Wang, Jiayi and Dong, Junyu and Du, Qian},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Prototype-Based Information Compensation Network for Multi-Source Remote Sensing Data Classification}, 
  year={2025},
  volume={63},
  pages={1-15},