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Remote Sens. 2016, 8(9), 720; doi:10.3390/rs8090720

Detecting Terrain Stoniness From Airborne Laser Scanning Data †

1
Department of Information Technology, University of Turku, FI-20014 Turku, Finland
2
Geological Survey of Finland, P.O. Box 77, Lähteentie 2, 96101 Rovaniemi, Finland
This paper is an extended version of our paper published in Paavo Nevalainen, Ilkka Kaate, Tapio Pahikkala, Raimo Sutinen, Maarit Middleton, and Jukka Heikkonen. Detecting stony areas based on ground surface curvature distribution. In The 5th International Conference on Image Processing Theory, Tools and Applications, 2015.
*
Author to whom correspondence should be addressed.
Academic Editors: Jie Shan, Juha Hyyppä, Lars T. Waser and Prasad S. Thenkabail
Received: 29 June 2016 / Revised: 7 August 2016 / Accepted: 17 August 2016 / Published: 31 August 2016
(This article belongs to the Special Issue Airborne Laser Scanning)
View Full-Text   |   Download PDF [3573 KB, uploaded 31 August 2016]   |  

Abstract

Three methods to estimate the presence of ground surface stones from publicly available Airborne Laser Scanning (ALS) point clouds are presented. The first method approximates the local curvature by local linear multi-scale fitting, and the second method uses Discrete-Differential Gaussian curvature based on the ground surface triangulation. The third baseline method applies Laplace filtering to Digital Elevation Model (DEM) in a 2 m regular grid data. All methods produce an approximate Gaussian curvature distribution which is then vectorized and classified by logistic regression. Two training data sets consisted of 88 and 674 polygons of mass-flow deposits, respectively. The locality of the polygon samples is a sparse canopy boreal forest, where the density of ALS ground returns is sufficiently high to reveal information about terrain micro-topography. The surface stoniness of each polygon sample was categorized for supervised learning by expert observation on the site. The leave-pair-out (L2O) cross-validation of the local linear fit method results in the area under curve A U C = 0 . 74 and A U C = 0 . 85 on two data sets, respectively. This performance can be expected to suit real world applications such as detecting coarse-grained sediments for infrastructure construction. A wall-to-wall predictor based on the study was demonstrated. View Full-Text
Keywords: aerial laser scan; point cloud; digital elevation model; logistic regression; stoniness; natural resources; micro-topography; Gaussian curvature aerial laser scan; point cloud; digital elevation model; logistic regression; stoniness; natural resources; micro-topography; Gaussian curvature
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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

Nevalainen, P.; Middleton, M.; Sutinen, R.; Heikkonen, J.; Pahikkala, T. Detecting Terrain Stoniness From Airborne Laser Scanning Data †. Remote Sens. 2016, 8, 720.

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