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Remote Sens. 2016, 8(8), 648; doi:10.3390/rs8080648

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

1
Cooperative Research Centre for Spatial Information, Carlton VIC 3053, Australia
2
Department of Infrastructure Engineering, University of Melbourne, Parkville VIC 3010, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Jie Shan, Juha Hyyppä, Guoqing Zhou and Prasad S. Thenkabail
Received: 30 May 2016 / Revised: 29 July 2016 / Accepted: 3 August 2016 / Published: 8 August 2016
(This article belongs to the Special Issue Airborne Laser Scanning)
View Full-Text   |   Download PDF [3194 KB, uploaded 8 August 2016]   |  

Abstract

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. View Full-Text
Keywords: deconvolution; full-waveform; lidar; L-curve; sparse solution; target cross-section deconvolution; full-waveform; lidar; L-curve; sparse solution; target cross-section
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Azadbakht, M.; Fraser, C.S.; Khoshelham, K. A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data. Remote Sens. 2016, 8, 648.

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