TY - JOUR
T1 - A comparison of signal deconvolution algorithms based on small-footprint LiDAR waveform simulation
AU - Wu, Jiaying
AU - Van Aardt, J. A.N.
AU - Asner, Gregory P.
N1 - Funding Information:
Manuscript received August 10, 2010; revised November 16, 2010; accepted December 15, 2010. Date of publication February 16, 2011; date of current version May 20, 2011. This work was supported by Ph.D. research funding provided by the Rochester Institute of Technology.
PY - 2011/6
Y1 - 2011/6
N2 - A raw incoming (received) Light Detection And Ranging (LiDAR) waveform typically exhibits a stretched and relatively featureless character, e.g., the LiDAR signal is smeared and the effective spatial resolution decreases. This is attributed to a fixed time span allocated for detection, the sensor's variable outgoing pulse signal, receiver impulse response, and system noise. Theoretically, such a loss of resolution can be recovered by deconvolving the system response from the measured signal. In this paper, we present a comparative controlled study of three deconvolution techniques, namely, Richardson-Lucy, Wiener filter, and nonnegative least squares, in order to verify which method is quantitatively superior to others. These deconvolution methods were compared in terms of two use cases: 1) ability to recover the true cross-sectional profile of an illuminated object based on the waveform simulation of a virtual 3-D tree model and 2) ability to differentiate herbaceous biomass based on the waveform simulation of virtual grass patches. All the simulated waveform data for this study were derived via the "Digital Imaging and Remote Sensing Image Generation" radiative transfer modeling environment. Results show the superior performance for the Richardson-Lucy algorithm in terms of small root mean square error for recovering the true cross section, low false discovery rate for detecting the unobservable local peaks in the stretched raw waveforms, and high classification accuracy for differentiating herbaceous biomass levels.
AB - A raw incoming (received) Light Detection And Ranging (LiDAR) waveform typically exhibits a stretched and relatively featureless character, e.g., the LiDAR signal is smeared and the effective spatial resolution decreases. This is attributed to a fixed time span allocated for detection, the sensor's variable outgoing pulse signal, receiver impulse response, and system noise. Theoretically, such a loss of resolution can be recovered by deconvolving the system response from the measured signal. In this paper, we present a comparative controlled study of three deconvolution techniques, namely, Richardson-Lucy, Wiener filter, and nonnegative least squares, in order to verify which method is quantitatively superior to others. These deconvolution methods were compared in terms of two use cases: 1) ability to recover the true cross-sectional profile of an illuminated object based on the waveform simulation of a virtual 3-D tree model and 2) ability to differentiate herbaceous biomass based on the waveform simulation of virtual grass patches. All the simulated waveform data for this study were derived via the "Digital Imaging and Remote Sensing Image Generation" radiative transfer modeling environment. Results show the superior performance for the Richardson-Lucy algorithm in terms of small root mean square error for recovering the true cross section, low false discovery rate for detecting the unobservable local peaks in the stretched raw waveforms, and high classification accuracy for differentiating herbaceous biomass levels.
KW - Deconvolution
KW - Light Detection And Ranging (LiDAR)
KW - Richardson-Lucy (RL)
KW - Wiener filter (WF)
KW - nonnegative least squares (NNLS)
KW - simulation
KW - waveform
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U2 - 10.1109/TGRS.2010.2103080
DO - 10.1109/TGRS.2010.2103080
M3 - Article
AN - SCOPUS:79957635689
SN - 0196-2892
VL - 49
SP - 2402
EP - 2414
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 6 PART 2
M1 - 5714011
ER -