In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) and transformer architectures have each contributed to considerable advancements. CNNs possess potent local feature representation skills, whereas transformers excel in learning global features, offering a complementary strength. Nevertheless, both architectures are limited by static receptive fields, which hinder their accuracy in delineating subtle boundary discrepancies. To mitigate the identified limitations, we introduce a novel dual-branch adaptive convolutional transformer (DBACT) network architecture featuring an adaptive multi-head self-attention mechanism. The architecture begins with a triadic parallel stem structure for shallow feature extraction and reduction of the spectral dimension. A global branch with adaptive receptive fields performs high-level global feature extraction. Simultaneously, a local branch with a cross-attention module provides detailed local insights, enriching the global perspective. This methodical integration synergizes the advantages of both branches, capturing representative spatial-spectral features from HSI. Comprehensive evaluation across three benchmark datasets reveals that the DBACT model exhibits superior classification performance compared to leading-edge models.

Convolutional neural networks (CNNs) and transformers have achieved great success in hyperspectral image (HSI) classification. However, CNNs are inefficient in establishing long-range dependencies, and transformers may overlook some local information. To overcome these limitations, we propose a U-shaped convolution-aided transformer (UCaT) that incorporates convolutions into a novel transformer architecture to aid classification. The group convolution is employed as parallel local descriptors to extract detailed features, and then the multi-head self-attention recalibrates these features in consistent groups, emphasizing informative features while maintaining the inherent spectral–spatial data structure. Specifically, three components are constructed using particular strategies. First, the spectral groupwise self-attention (spectral-GSA) component is developed for spectral attention, which selectively emphasizes diagnostic spectral features among neighboring bands and reduces the spectral dimension. Then, the spatial dual-scale convolution-aided self-attention (spatial-DCSA) encoder and spatial convolution-aided cross-attention (spatial-CCA) decoder form a U-shaped architecture for per-pixel classifications over HSI patches, where the encoder utilizes a dual-scale strategy to explore information in different scales and the decoder adopts the cross-attention for information fusion. Experimental results on three datasets demonstrate that the proposed UCaT outperforms the competitors. Additionally, a visual explanation of the UCaT is given, showing its ability to build global interactions and capture pixel-level dependencies.