Local-Global Feature Extraction Network With Dynamic 3-D Convolution and Residual Attention Transformer for Hyperspectral Image Classification

作者：Qiqiang Chen, Zhengyang Li, Junru Yin, Wei Huang, Tianming Zhan · 发表于：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 年份：2025 · DOI：10.1109/jstars.2025.3556722 · 被引用次数：7 · 研究领域：Remote-Sensing Image Classification

Currently, convolutional neural network (CNN) and transformer-based hyperspectral image (HSI) classification methods have attracted significant attention owing to their effective feature representation capabilities. However, methods based on CNN pay insufficient attention to valuable pixels in 3-D HSI samples and cannot adapt to variations in these samples. Transformer-based methods also suffer from high computational complexity and a tendency for low-level spatial-spectral features of the shallow attention layer to vanish as the number of attention layers increases. To address these issues, we proposed a local–global feature extraction network with dynamic 3-D convolution and residual attention transformer (LGDRNet). The LGDRNet primarily consists of multiscale 3-D conv, dynamic local feature extraction, residual global feature extraction, and feature fusion modules. Specifically, a multiscale 3-D conv module is used for low-level multiscale spectral information extraction. Then, the dynamic local feature extraction module utilizes dynamic 3-D convolution, which can adapt to different samples. This allows the network to focus on valuable pixels in 3-D samples. The residual global feature extraction module utilizes a convolutional projection unit and convolutional multihead self-attention to reduce computational complexity. It employs a residual attention connection to enable the network to effectively transmit and accumulate attention information across consecutive multihead...