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A benchmark of lidar-based single tree detection methods using heterogeneous forest data from the alpine space
Lothar Eysn, Markus Hollaus, Eva Lindberg, Frédéric Berger, Jean-Matthieu Monnet, Michele Dalponte, Milan Kobal, Marco Antonio Pellegrini, Emanuele Lingua, Domen Mongus, Norbert Pfeifer, 2015, original scientific article

Abstract: In this study, eight airborne laser scanning (ALS)-based single tree detection methods are benchmarked and investigated. The methods were applied to a unique dataset originating from different regions of the Alpine Space covering different study areas, forest types, and structures. This is the first benchmark ever performed for different forests within the Alps. The evaluation of the detection results was carried out in a reproducible way by automatically matching them to precise in situ forest inventory data using a restricted nearest neighbor detection approach. Quantitative statistical parameters such as percentages of correctly matched trees and omission and commission errors are presented. The proposed automated matching procedure presented herein shows an overall accuracy of 97%. Method based analysis, investigations per forest type, and an overall benchmark performance are presented. The best matching rate was obtained for single-layered coniferous forests. Dominated trees were challenging for all methods. The overall performance shows a matching rate of 47%, which is comparable to results of other benchmarks performed in the past. The study provides new insight regarding the potential and limits of tree detection with ALS and underlines some key aspects regarding the choice of method when performing single tree detection for the various forest types encountered in alpine regions.
Keywords: single tree extraction, airborne laser scanning, forest inventory, comparative testing, co-registration, mountain forests, Alpine space, matching
Published: 21.06.2017; Views: 499; Downloads: 303
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Combined edge detection using wavelet transform and signal registration
Dušan Heric, Damjan Zazula, 2006, original scientific article

Abstract: This paper presents a novel edge detection algorithm, using Haar wavelet transform and signal registration. The proposed algorithm has two stages: (a) adaptive edge detection with the maximum entropy thresholding technique on time-scale plane and (b) edge linkage into a con tour line with signal registration in order to c1ose edge discontinuities and calculate a confidence index for contour linkages. This index measures the level of confidence in the linkage of two adjacent points in the con tour structure. Experimenting with synthetic images, we found out the lower level of confidence can be set to approximately e-2. The method was tested on 200 synthetic images at different signal-to-noise ratios (SNRs) and II clinical images. We assessed its reliability, accuracy and robustness using the mean absolute distance (MAD) metric and our confidence index. The results for MAD on synthetic images yield the mean of 0.7 points and standard deviation (std) of 0.14, while the mean confidence level is 0.48 with std of 0.19 (the values are averaged over SNRs from 3 to 50 dB each in 20 Monte-Carlo runs). Our assessment on clinical images, where the references were expert's annotations, give MAD equal1.36:1:: 0.36 (mean :1:: std) and confidence level equal 0.67 :1:: 0.25 (mean :1:: std).
Keywords: edge model, edge detection, wavelet transform, signal registration
Published: 31.05.2012; Views: 1598; Downloads: 85
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