HugIpuNet: Hybrid Updating Graph Motivated Illumination-Wise Property Unification Network for Hyperspectral and DSM Joint Classification

Hyperspectral images (HSIs) and digital surface models (DSMs) derived from light detection and ranging (LiDAR) data are packed with rich on-ground object characteristics, making their joint classification crucial for accurate remote sensing (RS) identification. However, variations in illumination properties hinder efficient and precise cross-modal information interaction. This phenomenon further exacerbates the difficulty in correctly identifying corresponding spectral curves, ultimately resulting in suboptimal performance. In this article, a hybrid updating graph motivated illumination-wise property unification network (HugIpuNet) is proposed to solve these challenges. Our core motivation is to unify global illumination descriptions with assistance from elevations for capturing precise multimodal properties from HSIs and DSMs derived from LiDAR data. HS spectral curves and LiDAR elevations interact constantly, considering their geological connection to strengthen their coupling capability. HugIpuNet further considers several illumination-wise mechanisms to address intricate and variable environmental conditions. The whole structure utilizes various hybrid updating (HU) graphs to enhance the attribute unification ability in complex environmental scenarios. Dynamic adjacent matrices are constructed to introduce uncertainty to limit the effect caused by description distortions. Experiments show an average 2.86% improvement in overall accuracy (OA) compared with multiple state-of-the-art approaches. The detailed datasets and codes will be available at https://github.com/weixinttt/HugIpuNet