# 3D Fourier-based Global Feature Extraction for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2603.16426 · HTML: https://mlsanity.com/paper/2603.16426/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 12 of 12)

- [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021)
- [MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2501.04944/) (2025)
- [A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models](https://mlsanity.com/paper/2404.14955/) (2024)
- [AdaptiveClick: Clicks-aware Transformer with Adaptive Focal Loss for Interactive Image Segmentation](https://mlsanity.com/paper/2305.04276/) (2023)
- [WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2408.01231/) (2024)
- [GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2407.08255/) (2024)
- [Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2408.01224/) (2024)
- [Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.01095/) (2024)
- [Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2201.01001/) (2022)
- [EnergyFormer: Energy Attention with Fourier Embedding for Hyperspectral Image Classification](https://mlsanity.com/paper/2503.08239/) (2025)
- [Transformer-Driven Active Transfer Learning for Cross-Hyperspectral Image Classification](https://mlsanity.com/paper/2411.18115/) (2024)
- [DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2412.17350/) (2024)

## Built on by (most cited first, 1 of 1)

- [Cosine-Normalized Attention for Hyperspectral Image Classification](https://mlsanity.com/paper/2604.01763/) (2026)

## Abstract (arXiv)

Hyperspectral image classification (HSIC) has been significantly advanced by deep learning methods that exploit rich spatial-spectral correlations. However, existing approaches still face fundamental limitations: transformer-based models suffer from poor scalability due to the quadratic complexity of self-attention, while recent Fourier transform-based methods typically rely on 2D spatial FFTs and largely ignore critical inter-band spectral dependencies inherent to hyperspectral data. To address these challenges, we propose Hybrid GFNet (HGFNet), a novel architecture that integrates localized 3D convolutional feature extraction with frequency-domain global filtering via GFNet-style blocks for efficient and robust spatial-spectral representation learning. HGFNet introduces three complementary frequency transforms tailored to hyperspectral imagery: Spectral Fourier Transform (a 1D FFT along the spectral axis), Spatial Fourier Transform (a 2D FFT over spatial dimensions), and Spatial-Spatial Fourier Transform (a 3D FFT jointly over spectral and spatial dimensions), enabling comprehensive and high-dimensional frequency modeling. The 3D convolutional layers capture fine-grained local spatial-spectral structures, while the Fourier-based global filtering modules efficiently model long-range dependencies and suppress noise. To further mitigate the severe class imbalance commonly observed in HSIC, HGFNet incorporates an Adaptive Focal Loss (AFL) that dynamically adjusts class-wise focusing and weighting, improving discrimination for underrepresented classes.