Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas

Alexander, Cici; Tansey, Kevin; Kaduk, Jörg; Holland, David A.; Tate, Nicholas

doi:10.1016/j.isprsjprs.2010.05.002

Cited by 101 publications

(95 citation statements)

References 12 publications

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“…Generally, a set of Gaussian functions is considered to both fit to the received backscattered waveform and to characterize each pulse shape in this approach. Gaussian decomposition and other similar decomposition methods have been extensively used to interpret targets related to the backscattered waveform in urban and forested areas [1,2,17,25]. However, this method is considered challenging in the case of echoes with low signal strength (low SNR) and it is deficient in its calculation of the cross-section in complex waveforms.…”

Section: Decomposition Methodsmentioning

confidence: 99%

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

2016

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Full-waveform lidar systems capture the complete backscattered signal from the interaction of the laser beam with targets located within the laser footprint. The resulting data have advantages over discrete return lidar, including higher accuracy of the range measurements and the possibility of retrieving additional returns from weak and overlapping pulses. In addition, radiometric characteristics of targets, e.g., target cross-section, can also be retrieved from the waveforms. However, waveform restoration and removal of the effect of the emitted system pulse from the returned waveform are critical for precise range measurement, 3D reconstruction and target cross-section extraction. In this paper, a sparsity-constrained regularization approach for deconvolution of the returned lidar waveform and restoration of the target cross-section is presented. Primal-dual interior point methods are exploited to solve the resulting nonlinear convex optimization problem. The optimal regularization parameter is determined based on the L-curve method, which provides high consistency in varied conditions. Quantitative evaluation and visual assessment of results show the superior performance of the proposed regularization approach in both removal of the effect of the system waveform and reconstruction of the target cross-section as compared to other prominent deconvolution approaches. This demonstrates the potential of the proposed approach for improving the accuracy of both range measurements and geophysical attribute retrieval. The feasibility and consistency of the presented approach in the processing of a variety of lidar data acquired under different system configurations is also highlighted.

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Section: Decomposition Methodsmentioning

confidence: 99%

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

2016

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“…In this context FW lidar adds the possibility of an analysis of the return signal to further analyze forest cover. The objective can be the classification of forest land-cover and species (Neuenschwander, 2009) or more forest-related parameters like biomass (Alexander et al, 2010;Baccini et al, 2012;Koch, 2010) or for inventory purposes (Anderson et al, 2008).…”

Section: Full Waveform Lidar In Forestrymentioning

confidence: 99%

Processing lidar waveform data for 3D visual assessment of forest environments

Pirotti

Guarnieri

Masiero

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The objective of this report is to present and discuss a work-flow for extracting, from full-waveform (FW) lidar data, formats which are compatible with common information systems (GIS) and statistical software packages. Full-waveform, specifically for forestry, got attention from the scientific community because a more in-depth analysis can add valuable information for classification and modelling of related variables (e.g. biomass). In order to assess if this is feasible and if the results are useful, the end-user has to deal with raw datasets from lidar sensors. In this study case we propose and test a work-flow which is implemented through a selfdeveloped software integrating ad-hoc C++ libraries and a graphical user interface for an easier approach by end-users. This software allows the user to add raw FW data and produce several products which can successively be easily imported in GIS or statistical software. To achieve this we used some state-of-the-art methods which have been extensively reported in literature and we discuss results and future developments. Results show that this software package can effectively work as a tool for linking raw FW data with forest-related spatial processing by providing punctual information directly derived from the FW data or area-based aggregated information for a more generalized description of the earth surface.

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“…Nowadays, laser altimeters placed on different platforms (airborne, satellite) have been widely used in lots of areas like mass balance estimation in Antarctica, vegetation vertical structure, long trend monitor of sea level and so on (Anderson et al, 2005;Alexander et al, 2010;Garvin et al, 1998;Gong et al, 2011;Hyyppä et al, 2001;Joerg, et al, 2015;Means and Acker, 1999;Naesset and Bjerknes, 2001;Nilsson, 1996;Smith et al, 1998;Tian et al,2015). For the reason that the measurement of altimeter can not only provide high precision elevation or distance information, it can also record the complete waveform of the backscattered signal echo, especially for the full-waveform laser altimeter (Mallet, 2009).…”

Section: Introductionmentioning

confidence: 99%

Simulation of Full-Waveform Laser Altimeter Echowaveform

Lv¹,

Tong²,

Liu³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Change of globe surface height is an important factor to study human living environment. The Geoscience Laser Altimeter System (GLAS) on ICESat is the first laser-ranging instrument for continuous global observations of the Earth. In order to have a comprehensive understanding of full-waveform laser altimeter, this study simulated the operating mode of ICESat and modeled different terrains' (platform terrain, slope terrain, and artificial terrain) echo waveforms based on the radar equation. By changing the characteristics of the system and the targets, numerical echo waveforms can be achieved. Hereafter, we mainly discussed the factors affecting the amplitude and size (width) of the echoes. The experimental results implied that the slope of the terrain, backscattering coefficient and reflectivity, target height, target position in the footprint and area reacted with the pulse all can affect the energy distribution of the echo waveform and the receiving time. Finally, Gaussian decomposition is utilized to decompose the echo waveform. From the experiment, it can be noted that the factors which can affect the echo waveform and by this way we can know more about large footprint full-waveform satellite laser altimeter.

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Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas

Cited by 101 publications

References 12 publications

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

Processing lidar waveform data for 3D visual assessment of forest environments

Simulation of Full-Waveform Laser Altimeter Echowaveform

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