Abstract
Abstract. 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.
Highlights
LiDAR (Light Detection and Ranging) is an active remote sensing system which can quickly provide three dimensional information of earth surface and object
The experiment of hidden targets extraction was not mentioned in these papers. This method is based on LiDAR point cloud data organized by KD-tree, after target segmentation, Hu invariable moments and flight characteristics could be combined to improve the airplane target recognition, even hidden airplanes mainly under the cover of canopy
In order to demonstrate the precision and robustness of the proposed method, another LiDAR point cloud data is shown in Figure 4, which contain 64954 points and with a density of 1.7 points/m2, and the results are shown in Figure 5
Summary
LiDAR (Light Detection and Ranging) is an active remote sensing system which can quickly provide three dimensional information of earth surface and object. Zhongliang et al (1992) proposed a new method for automatic ship classification using superstructure moment invariants In those papers, the classification methods of aircrafts or ships extracted from images by moment could be applied on LiDAR point cloud data, but point cloud data is discrete, there are some differences between the distance image and binary image, especially in the detail of the target outline. The experiment of hidden targets extraction was not mentioned in these papers This method is based on LiDAR point cloud data organized by KD-tree, after target segmentation, Hu invariable moments and flight characteristics could be combined to improve the airplane target recognition, even hidden airplanes mainly under the cover of canopy. These could be applied in target measuring and modelling of subsequent data processing
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