Multilevel Differential Aggregation With Gated Discrimination Network for Hyperspectral-LiDAR Joint Land Cover Classification

With the rapid development of Earth observation technologies, the integration of multi-source remote sensing data from diverse sensors offers greater potential for remote sensing image classification. Hyperspectral images (HSI) provide rich spectral and spatial information, while light detection and ranging (LiDAR) offers precise three-dimensional structural data. The effective fusion of these two modalities can significantly improve the accuracy of land cover classification. However, existing methods often preserve considerable redundant information during feature extraction and fusion, and the interaction among this redundant information significantly impairs the discriminative capability of the network. In addition, due to the irregular spatial distributions of multi-source remote sensing images, rule-based meshing methods often fail to model complex geometric structures and spatial dependencies, which reduces the ability of the network to perceive subtle spatial features across domains. To address the aforementioned challenges, a multilevel differential aggregation with gated discriminative network (MDAGNet) based on dynamic feature selection is proposed for joint land cover classification of HSI and LiDAR data. First, a multilevel differential aggregation module (ML-DAM) is proposed to extract spectral features from HSI and elevation features from LiDAR data separately, thereby generating more discriminative multilevel feature representations. Second, we meticulously design a gating mechanism-based adaptive discrimination module (ADM). This module adaptively distills useless redundant information through multi-scale gated discriminative operations, enabling deep fusion of critical detail features. Furthermore, we introduce a morphological strip convolution module (Morpho-SConv) to characterize the complex geometric spatial distribution in HSI, enhancing the ability of the network to perceive fine-grained features. Finally, we achieve adaptive fusion of multi-modal data through weight coupling and dynamic fusion mechanisms. Extensive experiments on three public datasets demonstrate that our method outperforms state-of-the-art approaches in both accuracy and generalization capability.