Saliency-Modulated Group Dynamic Attention for Hyperspectral Image Classification
Year: 2026
Type: article
Abstract: Hyperspectral image classification (HSIC) remains challenging due to severe spectral redundancy, complex spatial–spectral dependencies, and the scarcity of labeled samples. While convolutional neural networks (CNNs)-based frameworks improve local feature extraction, they struggle to capture long-range spatial–spectral interactions. Vision transformers offer a promising alternative; however, their uniform global self-attention mechanism can lead to attention dilution on dense spatial–spectral token grids, particularly under limited labeled-data conditions. To address these challenges, this article proposes SGDAFormer, a transformer-based framework for HSIC that incorporates the saliency-modulated group dynamic attention (SGDA) mechanism as its core contribution. SGDA partitions tokens into semantically coherent groups, applies intragroup self-attention, and adaptively reweights the group outputs via a learnable saliency gate, thereby suppressing redundant responses and emphasizing discriminative spatial–spectral regions. SGDA mitigates the representational inefficiency of uniform global self-attention and improves learning stability under limited annotation availability. Since SGDA operates on tokenized representations, we adopt a CNN-based tokenizer to produce spatially coherent spatial–spectral tokens and incorporate learnable positional embeddings to preserve relative spatial ordering within each patch. Extensive experiments on five public hyperspectral benchmarks under a strict low-label regime demonstrate that SGDAFormer consistently outperforms state-of-the-art methods, achieving an overall accuracy of 97% on the Pingan dataset.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,800
OpenAlex ID: W7164878655