# Prototype-Based Information Compensation Network for Multi-Source Remote Sensing Data Classification

arXiv: https://arxiv.org/abs/2505.04003 · HTML: https://mlsanity.com/paper/2505.04003/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 67 references that are in the map.
- First posted on arXiv 6 May 2025.

## Builds on (most cited first, 9 of 9)

- [Multimodal Fusion Transformer for Remote Sensing Image Classification](https://mlsanity.com/paper/2203.16952/) (2022)
- [Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks](https://mlsanity.com/paper/2309.16499/) (2023)
- [Classification of Hyperspectral and LiDAR Data Using Coupled CNNs](https://mlsanity.com/paper/2002.01144/) (2020)
- [SS-MAE: Spatial-Spectral Masked Auto-Encoder for Multi-Source Remote Sensing Image Classification](https://mlsanity.com/paper/2311.04442/) (2023)
- [Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification](https://mlsanity.com/paper/2301.03335/) (2023)
- [MSFMamba: Multi-Scale Feature Fusion State Space Model for Multi-Source Remote Sensing Image Classification](https://mlsanity.com/paper/2408.14255/) (2024)
- [Change Detection in Synthetic Aperture Radar Images Using a Dual-Domain Network](https://mlsanity.com/paper/2104.06699/) (2021)
- [FD2-Net: Frequency-Driven Feature Decomposition Network for Infrared-Visible Object Detection](https://mlsanity.com/paper/2412.09258/) (2024)
- [Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2201.08938/) (2022)

## Abstract (arXiv)

Multi-source remote sensing data joint classification aims to provide accuracy and reliability of land cover classification by leveraging the complementary information from multiple data sources. Existing methods confront two challenges: inter-frequency multi-source feature coupling and inconsistency of complementary information exploration. To solve these issues, we present a Prototype-based Information Compensation Network (PICNet) 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. Subsequently, cross-modal feature integration and alignment are achieved through cross-attention computation between the modality-specific prototype vectors and the raw feature representations. Extensive experiments on three public datasets demonstrate the significant superiority of our PICNet over state-of-the-art methods. The codes are available at https://github.com/oucailab/PICNet.