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

• URL: http://arxiv.org/abs/2603.16426v1
• Date: Tue, 17 Mar 2026 12:02:28 GMT
• Title: 3D Fourier-based Global Feature Extraction for Hyperspectral Image Classification
• Abstract summary: We propose Hybrid GFNet, a novel architecture that integrates localized 3D convolutional feature extraction with frequency-domain global filtering.<n>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.<n>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.
• Score: 4.139698879651128
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Adaptive Local Frequency Filtering for Fourier-Encoded Implicit Neural Representations [7.7579389797716365]
  We propose an adaptive local frequency filtering method for Fourier-encoded INRs.<n>The proposed method introduces a spatially varying parameter $(mathbfx)$ to modulate encoded Fourier components.<n>Experiments on 2D image fitting, 3D shape representation, and sparse data reconstruction demonstrate that the proposed method consistently improves reconstruction quality.
  arXiv  Detail & Related papers  (2026-04-03T08:04:30Z)
• Hybrid-Domain Synergistic Transformer for Hyperspectral Image Denoising [3.7420648748151977]
  This paper proposes an HSI denoising framework, Hybrid-Domain Synergistic Transformer Network (HDST), based on frequency domain enhancement and multiscale modeling.<n> Experiments on both real and synthetic datasets demonstrate that HDST significantly improves denoising performance.<n>This research provides new insights and a universal framework for addressing complex noise coupling issues in HSI and other high-dimensional visual data.
  arXiv  Detail & Related papers  (2025-07-27T01:45:29Z)
• 3D Wavelet Convolutions with Extended Receptive Fields for Hyperspectral Image Classification [12.168520751389622]
  Deep neural networks face numerous challenges in hyperspectral image classification.<n>This paper proposes WCNet, an improved 3D-DenseNet model integrated with wavelet transforms.<n> Experimental results demonstrate superior performance on the IN, UP, and KSC datasets.
  arXiv  Detail & Related papers  (2025-04-15T01:39:42Z)
• SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling [79.56581753856452]
  SparseFlex is a novel sparse-structured isosurface representation that enables differentiable mesh reconstruction at resolutions up to $10243$ directly from rendering losses.<n>By enabling high-resolution, differentiable mesh reconstruction and generation with rendering losses, SparseFlex significantly advances the state-of-the-art in 3D shape representation and modeling.
  arXiv  Detail & Related papers  (2025-03-27T17:46:42Z)
• DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification [3.271106943956333]
  Hyperspectral image classification (HSIC) has gained significant attention because of its potential in analyzing high-dimensional data with rich spectral and spatial information.<n>We propose the Differential Spatial-Spectral Transformer (DiffFormer) to address the inherent challenges of HSIC, such as spectral redundancy and spatial discontinuity.<n>Experiments on benchmark hyperspectral datasets demonstrate the superiority of DiffFormer in terms of classification accuracy, computational efficiency, and generalizability.
  arXiv  Detail & Related papers  (2024-12-23T07:21:41Z)
• FOF-X: Towards Real-time Detailed Human Reconstruction from a Single Image [64.96903230497755]
  We introduce FOF-X for real-time reconstruction of detailed human geometry from a single image.<n>The core of FOF is to factorize a 3D occupancy field into a 2D vector field, retaining topology and spatial relationships within the 3D domain.<n>Based on FOF, we design a new reconstruction framework, FOF-X, to avoid the performance degradation caused by texture and lighting.
  arXiv  Detail & Related papers  (2024-12-08T14:46:29Z)
• Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising [64.11157141177208]
  We propose a spectral enhanced rectangle Transformer to model the spatial and spectral correlation in hyperspectral images. For the former, we exploit the rectangle self-attention horizontally and vertically to capture the non-local similarity in the spatial domain. For the latter, we design a spectral enhancement module that is capable of extracting global underlying low-rank property of spatial-spectral cubes to suppress noise.
  arXiv  Detail & Related papers  (2023-04-03T09:42:13Z)
• Dynamic Temporal Filtering in Video Models [128.02725199486719]
  We present a new recipe of temporal feature learning, namely Dynamic Temporal Filter (DTF) DTF learns a specialized frequency filter for every spatial location to model its long-range temporal dynamics. It is feasible to plug DTF block into ConvNets and Transformer, yielding DTF-Net and DTF-Transformer.
  arXiv  Detail & Related papers  (2022-11-15T15:59:28Z)
• Deep Fourier Up-Sampling [100.59885545206744]
  Up-sampling in the Fourier domain is more challenging as it does not follow such a local property. We propose a theoretically sound Deep Fourier Up-Sampling (FourierUp) to solve these issues.
  arXiv  Detail & Related papers  (2022-10-11T06:17:31Z)
• Global Filter Networks for Image Classification [90.81352483076323]
  We present a conceptually simple yet computationally efficient architecture that learns long-term spatial dependencies in the frequency domain with log-linear complexity. Our results demonstrate that GFNet can be a very competitive alternative to transformer-style models and CNNs in efficiency, generalization ability and robustness.
  arXiv  Detail & Related papers  (2021-07-01T17:58:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.