An Attention-Enhanced Feature Fusion Network (AeF2N) for Hyperspectral Image Classification

In recent years, numerous deep learning (DL)-based frameworks have been proposed for hyperspectral image classification (HSIC). Considering a large number of spectral bands of hyperspectral images (HSIs), it is still challenging to effectively utilize the spectral information and achieve accurate classification when few training samples are available. To make full use of the spectral-spatial information in HSIs with few training samples, in this letter, we propose a lightweight end-to-end attention-enhanced feature fusion network (AeF(2)N). The proposed AeF(2)N consists of four sequential stages, i.e., spectral feature augmentation, spatial contextual feature interaction, spectral feature augmentation, and classification. The first and third stages are used to capture and augment the discriminative spectral features, while the second stage is used to capture spatial information. Notably, two novel attention blocks, spectral augmentation attention (SAA) and spatial integration attention (SIA) are interactively introduced to capture significant spectral and spatial information, respectively. Based on the proposed spectral and spatial feature discrimination stages, the AeF(2)N effectively identifies both spectrally significant (e.g., irregular small objects) and spatially significant (e.g., specific-shaped objects) land objects with high accuracy. Experimental results obtained on three benchmark hyperspectral datasets demonstrate the superiority of the proposed approach compared with six state-of-the-art DL-based methods in terms of higher classification accuracy and efficiency.