Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 219-225, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-219-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
12 Sep 2017
A CURVATURE BASED ADAPTIVE NEIGHBORHOOD FOR INDIVIDUAL POINT CLOUD CLASSIFICATION
E. He1, Q. Chen1,2, H. Wang1, and X. Liu1 1Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
2Center for Spatial Information Science, The University of Tokyo, Kashiwanoha, Kashiwa-shi, Chiba 277-8568, Japan
Keywords: Curvature, Adaptive Neighborhood, Individual Point Cloud Classification Abstract. As a key step in 3D scene analysis, point cloud classification has gained a great deal of concerns in the past few years. Due to the uneven density, noise and data missing in point cloud, how to automatically classify the point cloud with a high precision is a very challenging task. The point cloud classification process typically includes the extraction of neighborhood based statistical information and machine learning algorithms. However, the robustness of neighborhood is limited to the density and curvature of the point cloud which lead to a label noise behavior in classification results. In this paper, we proposed a curvature based adaptive neighborhood for individual point cloud classification. Our main improvement is the curvature based adaptive neighborhood method, which could derive ideal 3D point local neighborhood and enhance the separability of features. The experiment result on Oakland benchmark dataset shows that the proposed method can effectively improve the classification accuracy of point cloud.
Conference paper (PDF, 1684 KB)


Citation: He, E., Chen, Q., Wang, H., and Liu, X.: A CURVATURE BASED ADAPTIVE NEIGHBORHOOD FOR INDIVIDUAL POINT CLOUD CLASSIFICATION, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 219-225, https://doi.org/10.5194/isprs-archives-XLII-2-W7-219-2017, 2017.

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