Journal Article

Dual-Branch Spatial–Spectral Transformer with Similarity Propagation for Hyperspectral Image Classification

Wang, Heng, Wang, Liguo, Wen, Teng

2025

Overview

In recent years, Vision Transformers (ViTs) have gained significant traction in the field of hyperspectral image classification due to their advantages in modeling long-range dependency relationships between spectral bands and spatial pixels. However, after stacking multiple Transformer encoders, challenges pertaining to information degradation may emerge during the forward propagation. That is to say, existing Transformer-based methods exhibit certain limitations in retaining and effectively utilizing information throughout their forward transmission. To tackle these challenges, this paper proposes a novel dual-branch spatial–spectral Transformer model that incorporates similarity propagation (DBSSFormer-SP). Specifically, this model first employs a Hybrid Pooling Spatial Channel Attention (HPSCA) module to integrate global information by pooling across different dimensional directions, thereby enhancing its ability to extract salient features. Secondly, we introduce a mechanism for transferring similarity attention that aims to retain and strengthen key semantic features, thus mitigating issues associated with information degradation. Additionally, the Spectral Transformer (SpecFormer) module is employed to capture long-range dependencies among spectral bands. Finally, the extracted spatial and spectral features are fed into a multilayer perceptron (MLP) module for classification. The proposed method is evaluated against several mainstream approaches on four public datasets. Experimental results demonstrate that DBSSFormer-SP exhibits excellent classification performance.

• Publisher: MDPI AG
• Subject: Accuracy / Algorithms / Band spectra / Classification / Computer vision / Deep learning / Degradation / Design / Electric transformers / Feature extraction / hyperspectral image classification / Hyperspectral imaging / Image classification / Image processing / Information processing / Machine learning / Modules / Morphology / Multilayer perceptrons / Neural networks / Salience / Similarity / similarity propagation / spatial and spectral features / Spectral bands / Transformer