EnergyFormer: Energy Attention with Fourier Embedding for Hyperspectral Image Classification
Journal:
arXiv
Published Date:
Mar 11, 2025
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.
