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

TL;DR

This paper introduces HGFNet, a hybrid deep learning architecture that combines 3D convolutional features with multi-dimensional Fourier transforms and adaptive loss to improve hyperspectral image classification by capturing spatial-spectral dependencies efficiently.

Contribution

HGFNet is the first to integrate 3D local convolutional features with multi-dimensional Fourier transforms and adaptive loss for hyperspectral classification.

Findings

Enhanced spectral-spatial feature extraction

Improved classification accuracy on hyperspectral datasets

Effective handling of class imbalance

Abstract

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.