Hierarchical heterogeneous graph learning for color-missing ALS pointcloud segmentation

Abstract

Semantically segmented aerial laser scanning (ALS) pointcloud is crucial for remote sensing applications, offering advantages over aerial images in describing complex topography of vegetation-covered areas due to its ability to penetrate through vegetation. While many ALS pointcloud segmentation methods emphasize the importance of color information for accurate segmentation and colorize the ALS pointcloud with aerial images, they often overlook the fact that some points in vegetation-covered areas are occluded and cannot be observed in aerial images. Consequently, these methods may assign inaccurate colors to these points, resulting in degraded segmentation performance. To address this issue, this paper proposes a Hierarchical Heterogeneous Graph Learning (HHGL) algorithm. HHGL tackles the problem by treating the colors of occluded points (referred to as “color-missing points”) as missing values and compensating for them based on the local and global geometric relationships among color-missing points and color-observed points. Specifically, the proposed algorithm first models the local geometric relationships as a heterogeneous graph, which aggregates the features of adjacent color-observed points to make up for the missing colors. Additionally, the global geometric relationships are represented as a hierarchical structure, refining the aggregated features and capturing long-range dependencies among color-missing points to facilitate segmentation. Experimental results on real-world datasets validate the effectiveness and robustness of the proposed HHGL algorithm.