Results 141 to 150 of about 1,272 (178)

Decomposing LiDAR waveforms with nonparametric classification methods

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
Waveform decomposition is an important step in full-waveform LiDAR remote sensing. Under the Gaussian Mixture Model, the conventional parametric classification algorithm of Expectation-Maximization (EM) is among the most widely applied ones to decompose the waveforms.
Qinghua Li, Serkan Ural, Jie Shan
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Simulated lidar waveforms for understanding factors affecting waveform shape

SPIE Proceedings, 2011
Full-waveform LIDAR is a technology which enables the analysis of the 3-D structure and arrangement of objects. An in-depth understanding of the factors that affect the shape of the full-waveform signal is required in order to extract as much information as possible from the signal.
Angela M. Kim, Richard C. Olsen
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Analysis and exploitation of lidar waveform data

Laser Radar Technology and Applications XXIV, 2019
Data from the Optech Titan airborne laser scanner were collected over Monterey, CA, in three wavelengths (532 nm, 1064 nm, and 1550 nm), in October 2016, by the National Center for Airborne LiDAR Mapping (NCALM). Lidar waveform data at 532 nm from the Optech Titan were analyzed for data collected over the forested area at the Pont Lobos State Park ...
Richard C. Olsen, Andrew S. Davis, Jeremy P. Metcalf   +2 more
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A Photon-Counting Full-Waveform Lidar*

Chinese Physics Letters, 2019
Abstract We present the results of using a photon-counting full-waveform lidar to obtain detailed target information with high accuracy. The parameters of the waveforms (i.e., vertical structure, peak position, peak amplitude, peak width and backscatter cross section) are derived with a high resolution limit of 31 mm ...
Bing-Cheng Du, Zhao-Hui Li, Guang-Yue Shen, Tian-Xiang Zheng, Hai-Yan Zhang, Lei Yang, Guang Wu   +6 more
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Exponential decomposition for waveform LiDAR processing

AOPC 2019: Optical Sensing and Imaging Technology, 2019
LiDAR (light detection and ranging) has recently been recognized as one of the most promising remote sensing techniques due to its excellent performance in the detection of forest inventory, topographic mapping, and automatic driving. Compared to discrete return systems that can provide only range information with a limited number of backscatter ...
Lu Xu, Jiancheng Lai, Zhenhua Li
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Compression strategies for LiDAR waveform cube

ISPRS Journal of Photogrammetry and Remote Sensing, 2015
Full-waveform LiDAR data (FWD) provide a wealth of information about the shape and materials of the surveyed areas. Unlike discrete data that retains only a few strong returns, FWD generally keeps the whole signal, at all times, regardless of the signal intensity.
Grzegorz Jóźków, Charles Toth, Mihaela Quirk, Dorota Grejner-Brzezinska   +3 more
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Interference-robust waveform for LiDAR

2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), 2023
Daniel Bastos, Bruno Faria 0005, Paulo P. Monteiro, Arnaldo S. R. Oliveira, Miguel V. Drummond   +4 more
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Classification of lidar waveforms by neural networks

1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96, 2002
A neural network scheme for the classification of lidar waveforms for the LARSEN 500 airborne system is proposed. It uses a single layer of linear neurons for classification of waveforms containing milt of various densities into a number of clusters.
D. Bhattacharya, R. Pillai, A. Antoniou
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Visualization and analysis of lidar waveform data

SPIE Proceedings, 2017
LiDAR waveform analysis is a relatively new activity in the area of laser scanning. The work described here is an exploration of a different approach to visualization and analysis, following the structure that has evolved for the analysis of imaging spectroscopy data (hyperspectral imaging).
Richard C. Olsen, Jeremy P. Metcalf
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Terrain slope calculation from waveform of airborne LiDAR

2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
In this paper, a novel physical model and its mathematical expressions were advanced to estimate the average slope of land by using LVIS data, which was collected in mountain area, California, USA. The proposed expressions were related not only to the laser scan angle and the laser divergence, but also to the pulse delay and the pulse width.
Xiaolu Li 0001, Lijun Xu 0001, Changwei Wang 0003   +2 more
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