VSLNet: Multimodal Data Fusion Network for Tree Species Classification in Overhead Transmission Line Corridors

The classification of tree species for overhead transmission lines (OHTL) is of great significance, facilitating the the timely removal of safety hazards posed by trees on power lines. Addressing the challenges in classifying OHTL line tree species, including subtle differences in target shape appearance, densely distributed targets, and limited representation in single-modal data, this article proposes a tree species classification network, VSLNet, based on multimodal data fusion. VSLNet constructs three asymmetric branches, which automatically select more discriminative features among spectra during spectral information processing, and jointly guide the extracted visible light information, ensuring global and local consistency for accurate multispectral classification. Furthermore, in LiDAR processing, the segmentation of individual trees contributes data such as tree height and crown diameter, and seamlessly integrates GPS data with multispectral classification results. Experimental results demonstrate that VSLNet is a feasible and reliable solution for tree classification, with potential applicability to other multimodal tasks.