Center-Guided Dynamic Convolutional Network for Hyperspectral Image Classification

In hyperspectral image classification, effectively modeling the relationship between a target pixel and its surrounding context remains challenging. While Transformer-based approaches achieve promising results through self-attention mechanisms, their quadratic computational complexity limits practical deployment. In this article, we propose a novel center-guided dynamic convolutional network (CGDC-Net) that enhances convolutional neural network architectures with input-adaptive convolutions and center-guided attention mechanisms. CGDC-Net introduces the central dynamic convolution (CDConv) module that employs dynamically modulated kernels adapting to each input sample based on multiscale contextual information centered around the target pixel. Through efficient tensor decomposition, CDConv generates sample-specific kernel modulations while maintaining computational efficiency. Building upon CDConv, we further incorporate center-guided convolutional block attention that integrates center-guided channel attention and center-guided spatial attention to explicitly emphasize the target pixel while capturing discriminative spectral–spatial features. By combining dynamic convolutions with attention mechanisms, CGDC-Net achieves enhanced feature representation while maintaining CNN's computational efficiency. Extensive experiments on three public HSI datasets demonstrate that CGDC-Net outperforms state-of-the-art methods.