Share Email Print

Journal of Applied Remote Sensing

Landcover classification of small-footprint, full-waveform lidar data
Author(s): Amy L. Neuenschwander; Lori A. Magruder; Marcus Tyler
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Full-waveform lidar data are emerging into the commercial sector and provide a unique ability to characterize the landscape. The returned laser waveforms indicate specific reflectors within the footprint (vertical structure), while the shape of the return convolves surface reflectance and physical topography. These data are especially effective in vegetative regions with respect to canopy structure characterization. The objective of this research is to evaluate the performance of waveform-derived parameters as input into a supervised classifier. Extracted waveform metrics include Gaussian amplitude, Gaussian standard deviation, canopy energy, ground energy, total waveform energy, ratio between canopy and ground energy, rise time to the first peak, fall time of the last peak, and height of median energy (HOME). The classifier utilizes a feature selection methodology which provides information on the value of waveform parameters for discriminating between class pairs. For this study area, energy ratio and Gaussian amplitude were selected most frequently, but rise time and fall time were also important for discriminating different tree types and densities. The lidar classification accuracy for this study area was 85.8% versus 71.2% for Quickbird imagery. Since the lidar-based input data are structural parameters derived from the waveforms, the classification is improved for classes that are spectrally similar but structurally different.

Paper Details

Date Published: 1 August 2009
PDF: 13 pages
J. Appl. Rem. Sens. 3(1) 033544 doi: 10.1117/1.3229944
Published in: Journal of Applied Remote Sensing Volume 3, Issue 1
Show Author Affiliations
Amy L. Neuenschwander, The Univ. of Texas at Austin (United States)
Lori A. Magruder, The Univ. of Texas at Austin (United States)
Marcus Tyler, The Univ. of Texas at Austin (United States)

© SPIE. Terms of Use
Back to Top