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A Recognition Method for Airplane Targets Using 3D Point Cloud Data : Volume Xxxix-b3, Issue 1 (30/07/2012)

By Zhou, M.

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Book Id: WPLBN0004016492
Format Type: PDF Article :
File Size: Pages 5
Reproduction Date: 2015

Title: A Recognition Method for Airplane Targets Using 3D Point Cloud Data : Volume Xxxix-b3, Issue 1 (30/07/2012)  
Author: Zhou, M.
Volume: Vol. XXXIX-B3, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus Publications
Publication Date:
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

Zhou, M., Peng, Z., Li, C., Tang, L., & Li, J. (2012). A Recognition Method for Airplane Targets Using 3D Point Cloud Data : Volume Xxxix-b3, Issue 1 (30/07/2012). Retrieved from

Description: Academy of Opto-Electronics, Chinese Academy of Sciences, No.9, Dengzhuang Nanlu, Beijing, China. LiDAR is capable of obtaining three dimension coordinates of the terrain and targets directly and is widely applied in digital city, emergent disaster mitigation and environment monitoring. Especially because of its ability of penetrating the low density vegetation and canopy, LiDAR technique has superior advantages in hidden and camouflaged targets detection and recognition. Based on the multi-echo data of LiDAR, and combining the invariant moment theory, this paper presents a recognition method for classic airplanes (even hidden targets mainly under the cover of canopy) using KD-Tree segmented point cloud data. The proposed algorithm firstly uses KD-tree to organize and manage point cloud data, and makes use of the clustering method to segment objects, and then the prior knowledge and invariant recognition moment are utilized to recognise airplanes. The outcomes of this test verified the practicality and feasibility of the method derived in this paper. And these could be applied in target measuring and modelling of subsequent data processing.



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