Multi-Scale Lightweight Spectral Attention Network for Hyperspectral Image Classification

Hyperspectral remote sensing technology provides a unique data foundation for accurate land-cover identification by acquiring continuous spectral information of land cover. To address the issues of large parameter count, high computational complexity, and insufficient utilization of spectral–spatial features in existing hyperspectral image classification methods, a Multi-Scale Lightweight Spectral Attention Network (MLSANet) is proposed. The proposed network consists of three components: a lightweight feature extraction layer, an efficient attention enhancement layer, and a structure-aware representation layer. The lightweight feature extraction layer reduces computational overhead through principal component analysis (PCA) for dimensionality reduction, spatial downsampling, depthwise separable convolution, and low-rank factorized convolution. The efficient attention enhancement layer comprises two novel modules: the Lightweight Pruning Parameterized Attention (LPPA) module and the Pixel-wise Adaptive Dilated Attention (PADA) module. The LPPA module prunes features based on an energy function and introduces a learnable scaling factor to adaptively enhance salient features. The PADA module utilizes pixel-wise attention and multi-scale dilated convolution to finely fuse spectral–spatial features. Furthermore, the structure-aware representation layer integrates a graph convolutional network with a lightweight multi-layer perceptron to model global structural relationships, thereby further improving feature discriminability. Experimental results on three public datasets, including Indian Pines, Pavia Centre, and Salinas, demonstrate that the proposed MLSANet model achieves competitive classification accuracy compared with recent representative methods, attaining overall accuracies of 99.01%, 98.66%, and 95.88%, respectively, while simultaneously exhibiting lower parameter counts and computational costs. This confirms the favorable trade-off between accuracy and efficiency achieved by our model.