# Dynamic Frequency Feature Fusion Network for Multi-Source Remote Sensing Data Classification

arXiv: https://arxiv.org/abs/2507.04510 · HTML: https://mlsanity.com/paper/2507.04510/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 6 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [FFT-based Dynamic Token Mixer for Vision](https://mlsanity.com/paper/2303.03932/) (2023)
- [Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification](https://mlsanity.com/paper/2301.03335/) (2023)
- [Frequency-Compensated Network for Daily Arctic Sea Ice Concentration Prediction](https://mlsanity.com/paper/2504.16745/) (2025)

## Built on by (most cited first, 3 of 3)

- [Representative Spectral Correlation Network for Multi-source Remote Sensing Image Classification](https://mlsanity.com/paper/2604.27323/) (2026)
- [Exploring the Potential of Contrastive Language-Image Pre-training for Multi-Source Remote Sensing Data](https://mlsanity.com/paper/2609.03391/) (2026)
- [Semantic-Guided Fusion Network for Multi-Source Remote Sensing Image Classification](https://mlsanity.com/paper/2609.27854/) (2026)

## Abstract (arXiv)

Multi-source data classification is a critical yet challenging task for remote sensing image interpretation. Existing methods lack adaptability to diverse land cover types when modeling frequency domain features. To this end, we propose a Dynamic Frequency Feature Fusion Network (DFFNet) for hyperspectral image (HSI) and Synthetic Aperture Radar (SAR) / Light Detection and Ranging (LiDAR) data joint classification. Specifically, we design a dynamic filter block to dynamically learn the filter kernels in the frequency domain by aggregating the input features. The frequency contextual knowledge is injected into frequency filter kernels. Additionally, we propose spectral-spatial adaptive fusion block for cross-modal feature fusion. It enhances the spectral and spatial attention weight interactions via channel shuffle operation, thereby providing comprehensive cross-modal feature fusion. Experiments on two benchmark datasets show that our DFFNet outperforms state-of-the-art methods in multi-source data classification. The codes will be made publicly available at https://github.com/oucailab/DFFNet.