KD-Tree-Based Euclidean Clustering for Tomographic SAR Point Cloud Extraction and Segmentation

Abstract

Tomographic Synthetic Aperture Radar (TomoSAR) has three-dimensional imaging capability. Never- theless, its building structure is full of considerable noise and wrong targets owing to the baseline distribution and the algorithm restrictions. Accordingly, efficiently extracting and segmenting buildings from SAR point clouds with huge data is a critical issue. According to the characteristics of building facade point clouds, the KD-Tree-based Euclidean clustering is introduced as a fast-clustering method for better extracting and segmenting individual building facades. At first, a simple morphological filter (SMRF) is utilized to identify the ground and non-ground point clouds. The building facades are then extracted from the non-ground point clouds based on density information. Subsequently, the KD-Tree-based Euclidean clustering method denoises the building facades and segments them into individual facades. Finally, a second Euclidean clustering is performed for each of these facades to denoise them again. Experimental results indicate that the presented approach can prepare accurate and efficient extraction and segmentation results for complex urban scenes.