A Spatial-Spectral-Frequency Interactive Network for Multimodal Remote Sensing Classification

2026-07-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Environmental Science & Earth Systems · Depth: Expert, extended

Summary

S2Fin is a novel deep learning framework designed for multimodal remote sensing image classification, specifically addressing the challenge of extracting structural and detail features from heterogeneous and redundant data. The network integrates pairwise fusion modules across spatial, spectral, and frequency domains. Key components include a high-frequency sparse enhancement transformer (HFSET) that optimizes high-frequency filter parameters using sparse spatial-spectral attention, a two-level spatial-frequency fusion strategy comprising an adaptive frequency channel module (AFCM) and a high-frequency resonance mask (HFRM), and a spatial-spectral attention fusion (SSAF) module. Experiments on four benchmark multimodal datasets (Houston 2013 HSI+LiDAR, Augsburg HSI+SAR, Yellow River Estuary HSI+SAR, LCZ HK MSI+SAR) with limited labeled data demonstrate S2Fin's superior classification performance, outperforming existing advanced classification methods by up to 2.66% in overall accuracy. The code is available at https://github.com/HaoLiu-XDU/SSFin.

Key takeaway

For Machine Learning Engineers developing remote sensing classification models with limited labeled data, S2Fin offers a robust approach. You should consider integrating frequency domain learning and multi-level spatial-frequency fusion to enhance detail extraction and improve classification accuracy, particularly for challenging, similar land-cover categories. This method consistently outperforms existing advanced models, even with few-shot training, making it valuable for resource-constrained projects.

Key insights

Integrating spatial, spectral, and frequency domain fusion enhances multimodal remote sensing classification, especially with limited labels.

Principles

• High-frequency components capture critical details like edges and textures.
• Frequency domain learning reduces reliance on large training datasets.
• Phase information is a key carrier of image structural information.

Method

S2Fin uses HFSET for spectral-frequency fusion, AFCM and HFRM for two-level spatial-frequency fusion, and SSAF for spatial-spectral fusion, enhancing high-frequency details and inter-domain interaction.

In practice

• Apply frequency domain transforms to disentangle shared and modality-specific features.
• Prioritize high-frequency components for fine-grained detail extraction.
• Use sparse attention to optimize high-frequency filter parameters.

Topics

• Multimodal Remote Sensing
• Deep Learning
• Frequency Domain Analysis
• Image Classification
• Hyperspectral Imaging
• SAR Data Fusion

Code references

• HaoLiu-XDU/SSFin

Best for: Computer Vision Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.