NDLI: Hidden target detection from the multi-echo small-footprint LiDAR point clouds

Content Provider	IEEE Xplore Digital Library
Author	Yiping Chen Cheng Wang Li, J.
Copyright Year	2015
Description	Author affiliation: Fujian Key Lab. of Sensing & Comput. for Smart Cities, Xiamen Univ., Xiamen, China (Yiping Chen; Cheng Wang; Li, J.)
Abstract	We propose a new approach for hidden or potential object detection behind the vegetation based on the multi-echo small-footprint of light detection and ranging(LiDAR) point cloud. According to the specific characteristic that laser beam is able to penetrate foliage gaps, which offers the opportunity to perceive and detect the object invisible to the naked eye. First, the waveform sample data of the small footprint multi-echo liDAR uses a Gaussian fitting tool to curve fitting. Second, the peak detection of the waveform can be classified in statistical results of its wave numbers. Decomposition and correction are the processing for classifying based on the statistic data. After selecting the point clouds which are contained in the multi-peaks echoes, we obtain the tree and the embedded target behind it as well as ground elimination. The range of the waveform component is used to separate the penetrations material and the target by distance discriminant function. Experiments are implemented on the waveforms acquired by small-footprint LiDAR system VZ-1000 Sensor. The results indicate that the algorithm could provide an optimal solution for LiDAR waveform hidden target detection.
Starting Page	1
Ending Page	6
File Size	670654
Page Count	6
File Format	PDF
e-ISBN	9781467374781
DOI	10.1109/MMSP.2015.7340880
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Laser radar Vegetation Three-dimensional displays Laser beams Object detection Vegetation mapping Surface waves
Content Type	Text
Resource Type	Article

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