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Ground object recognition and segmentation from aerial image‐based 3D point cloud
Author(s) -
Ogura Katsuya,
Yamada Yuma,
Kajita Shugo,
Yamaguchi Hirozumi,
Higashino Teruo,
Takai Mineo
Publication year - 2019
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12232
Subject(s) - point cloud , computer science , artificial intelligence , computer vision , segmentation , cluster analysis , object (grammar) , ground truth , point (geometry) , grasp , context (archaeology) , pattern recognition (psychology) , geography , mathematics , geometry , archaeology , programming language
Several attempts have been made to grasp three‐dimensional (3D) ground shape from a 3D point cloud generated by aerial vehicles, which help fast situation recognition. However, identifying such objects on the ground from a 3D point cloud, which consists of 3D coordinates and color information, is not straightforward due to the gap between the low‐level point information (coordinates and colors) and high‐level context information (objects). In this paper, we propose a ground object recognition and segmentation method from a geo‐referenced point cloud. Basically, we rely on some existing tools to generate such a point cloud from aerial images, and our method tries to give semantics to each set of clustered points. In our method, firstly, such points that correspond to the ground surface are removed using the elevation data from the Geographical Survey Institute. Next, we apply an interpoint distance‐based clustering and color‐based clustering. Then, such clusters that share some regions are merged to correctly identify a cluster that corresponds to a single object. We have evaluated our method in several experiments in real fields. We have confirmed that our method can remove the ground surface within 20 cm error and can recognize most of the objects.