Implicit spatial-frequency fusion of hyperspectral and lidar data via kolmogorov-arnold networks

2026-05-15 · Source: cs.CV updates on arXiv.org · Field: Science & Research — Artificial Intelligence & Machine Learning, Environmental Science & Earth Systems, Engineering & Applied Sciences · Depth: Expert, long

Summary

The Implicit Frequency-Geometry Fusion Network (IFGNet) is a novel approach for hyperspectral image (HSI) and LiDAR data classification, designed to overcome limitations of traditional CNN and MLP methods in modeling complex spectral and geometric interactions. IFGNet utilizes Kolmogorov-Arnold Networks (KANs) with learnable spline-based functions to adaptively capture highly non-linear relationships. It introduces a LiDAR-guided implicit aggregation module that operates in both spatial and frequency domains, enhancing geometry-aware spatial representations and capturing global structural patterns. Tested on the Houston 2013 and MUUFL benchmarks, IFGNet consistently achieved superior performance, with overall accuracies of 99.37% and 92.67% respectively, outperforming existing fusion methods in overall accuracy, average accuracy, and Cohen's Kappa, while maintaining an efficient architecture.

Key takeaway

For Computer Vision Engineers working on remote sensing classification with HSI and LiDAR data, IFGNet offers a robust framework that significantly improves accuracy by leveraging Kolmogorov-Arnold Networks. You should explore KAN-based architectures for their ability to model complex, non-linear interactions and consider dual-domain implicit aggregation to capture both local geometric details and global frequency patterns in your fusion tasks.

Key insights

KANs enable adaptive, non-linear fusion of HSI and LiDAR data across spatial and frequency domains.

Principles

• Learnable spline functions enhance non-linear modeling.
• LiDAR guidance improves spatial consistency.
• Dual-domain fusion captures comprehensive representations.

Method

IFGNet encodes HSI and LiDAR features into a shared latent space using KANs, then implicitly aggregates them in spatial and frequency domains via LiDAR-guided KANs, before classification.

In practice

• Use KANs for complex multimodal data fusion.
• Integrate LiDAR for geometry-aware HSI processing.
• Consider frequency domain for global structural patterns.

Topics

• Kolmogorov-Arnold Networks
• Hyperspectral-LiDAR Fusion
• Implicit Aggregation
• Spatial-Frequency Domain Fusion
• Remote Sensing Classification

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.