EnergyFormer: Energy Attention with Fourier Embedding for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2503.08239v1
• Date: Tue, 11 Mar 2025 10:03:35 GMT
• Title: EnergyFormer: Energy Attention with Fourier Embedding for Hyperspectral Image Classification
• Abstract summary: High dimensionality and spectral variability of HSI data pose challenges for feature extraction and classification.<n>This paper presents EnergyFormer, a transformer-based framework designed to address these challenges.<n>EnergyFormer achieves exceptional overall accuracies of 99.28%, 98.63%, and 98.72%, respectively, outperforming state-of-the-art CNN, transformer, and Mamba-based models.
• Score: 3.105394345970172
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperspectral imaging (HSI) provides rich spectral-spatial information across hundreds of contiguous bands, enabling precise material discrimination in applications such as environmental monitoring, agriculture, and urban analysis. However, the high dimensionality and spectral variability of HSI data pose significant challenges for feature extraction and classification. This paper presents EnergyFormer, a transformer-based framework designed to address these challenges through three key innovations: (1) Multi-Head Energy Attention (MHEA), which optimizes an energy function to selectively enhance critical spectral-spatial features, improving feature discrimination; (2) Fourier Position Embedding (FoPE), which adaptively encodes spectral and spatial dependencies to reinforce long-range interactions; and (3) Enhanced Convolutional Block Attention Module (ECBAM), which selectively amplifies informative wavelength bands and spatial structures, enhancing representation learning. Extensive experiments on the WHU-Hi-HanChuan, Salinas, and Pavia University datasets demonstrate that EnergyFormer achieves exceptional overall accuracies of 99.28\%, 98.63\%, and 98.72\%, respectively, outperforming state-of-the-art CNN, transformer, and Mamba-based models. The source code will be made available at https://github.com/mahmad000.

Related papers

• SSFT: A Lightweight Spectral-Spatial Fusion Transformer for Generic Hyperspectral Classification [42.09644109233563]
  We propose the lightweight Spectral-Spatial Fusion Transformer (SSFT), which factorizes representation learning into spectral and spatial pathways.<n>We evaluate our SSFT on the challenging HSI-Benchmark, a heterogeneous multi-dataset benchmark covering earth observation, fruit condition assessment, and fine-grained material recognition.
  arXiv  Detail & Related papers  (2026-04-17T08:31:25Z)
• SpectrumFM: A New Paradigm for Spectrum Cognition [65.65474629224558]
  We propose a spectrum foundation model, termed SpectrumFM, which provides a new paradigm for spectrum cognition.<n>An innovative spectrum encoder that exploits the convolutional neural networks is proposed to effectively capture both fine-grained local signal structures and high-level global dependencies in the spectrum data.<n>Two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, are developed for pre-training SpectrumFM, enabling the model to learn rich and transferable representations.
  arXiv  Detail & Related papers  (2025-08-02T14:40:50Z)
• SpectrumFM: A Foundation Model for Intelligent Spectrum Management [99.08036558911242]
  Existing intelligent spectrum management methods, typically based on small-scale models, suffer from notable limitations in recognition accuracy, convergence speed, and generalization.<n>This paper proposes a novel spectrum foundation model, termed SpectrumFM, establishing a new paradigm for spectrum management.<n>Experiments demonstrate that SpectrumFM achieves superior performance in terms of accuracy, robustness, adaptability, few-shot learning efficiency, and convergence speed.
  arXiv  Detail & Related papers  (2025-05-02T04:06:39Z)
• Frequency-Aware Vision Transformers for High-Fidelity Super-Resolution of Earth System Models [0.0]
  Super-resolution methods tend to exhibit spectral bias, reconstructing low-frequency content more readily than valuable high-frequency details.<n>We introduce two frequency-aware frameworks: the Vision Transformer-Tuned Sinusoidal Implicit Representation (ViSIR) and the Vision Transformer Fourier Representation Network (ViFOR)<n>The results establish ViFOR as a state-of-the-art, scalable solution for climate data downscaling.
  arXiv  Detail & Related papers  (2025-02-18T01:52:41Z)
• FE-UNet: Frequency Domain Enhanced U-Net with Segment Anything Capability for Versatile Image Segmentation [50.9040167152168]
  We experimentally quantify the contrast sensitivity function of CNNs and compare it with that of the human visual system.<n>We propose the Wavelet-Guided Spectral Pooling Module (WSPM) to enhance and balance image features across the frequency domain.<n>To further emulate the human visual system, we introduce the Frequency Domain Enhanced Receptive Field Block (FE-RFB)<n>We develop FE-UNet, a model that utilizes SAM2 as its backbone and incorporates Hiera-Large as a pre-trained block.
  arXiv  Detail & Related papers  (2025-02-06T07:24:34Z)
• Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images [64.80875911446937]
  We propose a Correlation and Continuity Network (CCNet) for HSI reconstruction from RGB images. For the correlation of local spectrum, we introduce the Group-wise Spectral Correlation Modeling (GrSCM) module. For the continuity of global spectrum, we design the Neighborhood-wise Spectral Continuity Modeling (NeSCM) module.
  arXiv  Detail & Related papers  (2025-01-02T15:14:40Z)
• 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)
• Dual Selective Fusion Transformer Network for Hyperspectral Image Classification [34.7051033596479]
  Transformer has achieved satisfactory results in the field of hyperspectral image (HSI) classification.<n>Existing Transformer models face two key challenges when dealing with HSI scenes characterized by diverse land cover types and rich spectral information.<n>We propose a novel Dual Selective Fusion Transformer Network (DSFormer) for HSI classification.
  arXiv  Detail & Related papers  (2024-10-04T06:05:26Z)
• HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model [88.13261547704444]
  Hyper SIGMA is a vision transformer-based foundation model for HSI interpretation. It integrates spatial and spectral features using a specially designed spectral enhancement module. It shows significant advantages in scalability, robustness, cross-modal transferring capability, and real-world applicability.
  arXiv  Detail & Related papers  (2024-06-17T13:22:58Z)
• Learning transformer-based heterogeneously salient graph representation for multimodal remote sensing image classification [42.15709954199397]
  A transformer-based heterogeneously salient graph representation (THSGR) approach is proposed in this paper. First, a multimodal heterogeneous graph encoder is presented to encode distinctively non-Euclidean structural features from heterogeneous data. A self-attention-free multi-convolutional modulator is designed for effective and efficient long-term dependency modeling.
  arXiv  Detail & Related papers  (2023-11-17T04:06:20Z)
• SpectralGPT: Spectral Remote Sensing Foundation Model [60.023956954916414]
  A universal RS foundation model, named SpectralGPT, is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT) Compared to existing foundation models, SpectralGPT accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS big data. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS big data applications within the field of geoscience.
  arXiv  Detail & Related papers  (2023-11-13T07:09:30Z)
• ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution [76.7408734079706]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. We propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure.
  arXiv  Detail & Related papers  (2023-07-26T07:45:14Z)
• Hybrid Spectral Denoising Transformer with Guided Attention [34.34075175179669]
  We present a Hybrid Spectral Denoising Transformer (HSDT) for hyperspectral image denoising. Our HSDT significantly outperforms the existing state-of-the-art methods while maintaining low computational overhead.
  arXiv  Detail & Related papers  (2023-03-16T02:24:31Z)
• Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier [0.0]
  The high dimensionality of the hyperspectral images (HSI) is one of the main challenges for the analysis of the collected data. The existence of noisy, redundant and irrelevant bands increases the computational complexity. We propose a novel filter approach based on the spectral interaction measure and the support vector machines for dimensionality reduction.
  arXiv  Detail & Related papers  (2022-10-27T15:37:57Z)
• HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening [60.89777029184023]
  Pansharpening aims to fuse a registered high-resolution panchromatic image (PAN) with a low-resolution hyperspectral image (LR-HSI) to generate an enhanced HSI with high spectral and spatial resolution. Existing pansharpening approaches neglect using an attention mechanism to transfer HR texture features from PAN to LR-HSI features, resulting in spatial and spectral distortions. We present a novel attention mechanism for pansharpening called HyperTransformer, in which features of LR-HSI and PAN are formulated as queries and keys in a transformer, respectively.
  arXiv  Detail & Related papers  (2022-03-04T18:59:08Z)

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.