Skip to content
DSML.KZ
Publications

Article · IEEE Geoscience and Remote Sensing Letters · January 1, 2025

EnergyFormer: Energy Attention With Fourier Embedding for Hyperspectral Image Classification

Saad Sohail, Muhammad Usama, Usman Ghous, Manuel Mazzara, Salvatore Distefano, Muhammad Imran Ahmad

Manuel Mazzara
Open via DOIPDF

Abstract

Hyperspectral images (HSIs) capture detailed spectral–spatial information across hundreds of contiguous bands, enabling precise material identification in domains such as environmental monitoring, agriculture, and urban analysis. However, the high dimensionality and spectral variability inherent to HSIs present significant challenges for effective feature extraction and classification. This letter introduces EnergyFormer (EF), a transformer-based framework designed to overcome these limitations through three key innovations: 1) multihead energy attention (MHEA), which formulates an energy optimization mechanism to selectively enhance discriminative spectral–spatial features; 2) Fourier positional embedding (FoPE), which adaptively models long-range spectral and spatial dependencies; and 3) enhanced convolutional block attention module (ECBAM), which emphasizes informative wavelength bands and spatial structures for robust representation learning. Extensive experiments on the WHU-Hi-HanChuan, Salinas, and Pavia University datasets demonstrate that EF achieves superior classification performance with overall accuracies of 99.28%, 98.63%, and 98.72%, respectively, outperforming leading CNN-, transformer-, and Mamba-based models.

Remote-Sensing Image ClassificationAdvanced Neural Network ApplicationsAdvanced Image Fusion Techniqueshyperspectral image classificationtransformer modelsCBAM

More by Manuel Mazzara