A Large-to-Small Kernel Guided Multi-Level Aggregation Network for Hyperspectral Image Classification

Hyperspectral images (HSIs) simultaneously capture spatial and spectral information of a target. Compared with conventional visible-light images, HSI can offer higher spectral resolution that facilitates more detailed characterization. However, most existing HSI classification methods primarily emphasize low-level and high-level feature interactions while lacking effective encoding of mid-level interactions, which are often more discriminative. Moreover, HSI classification is typically conducted with patch-based inputs; although this approach facilitates the extraction of spatial information surrounding the central pixel, it tends to inadvertently dilute the network’s focus away from the central pixel. To address these challenges, we propose a Large-to-Small Kernel Guided Multi-Level Aggregation network (LSGMA). A novel Multi-Level Aggregation (MLA) module is designed, which enhances the network’s emphasis on mid-level features. It enables the simultaneous extraction of low-, mid-, and high-level features, thereby ultimately improving the classification accuracy of the model. In addition, a Large-to-Small Kernel Guided Focus (LSGF) module is introduced that more effectively captures spatial neighborhood cues while maintaining strong focus on central features. Extensive experiments on four public datasets demonstrate that the proposed LSGMA network achieves superior performance compared with several state-of-the-art methods.