scholarly journals Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data

2015 ◽  
Vol 7 (12) ◽  
pp. 17051-17076 ◽  
Author(s):  
Sudan Xu ◽  
George Vosselman ◽  
Sander Oude Elberink
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser

scholarly journals Classification of Needle Loss of Individual Scots Pine Trees by Means of Airborne Laser Scanning

Forests ◽  
2013 ◽  
Vol 4 (2) ◽  
pp. 386-403 ◽  
Author(s):  
Tuula Kantola ◽  
Mikko Vastaranta ◽  
Päivi Lyytikäinen-Saarenmaa ◽  
Markus Holopainen ◽  
Ville Kankare ◽  
...  
Keyword(s):  
Scots Pine ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Pine Trees ◽  
Needle Loss

scholarly journals Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data

2014 ◽  
Vol 6 (2) ◽  
pp. 1347-1366 ◽  
Author(s):  
Mariana Belgiu ◽  
Ivan Tomljenovic ◽  
Thomas Lampoltshammer ◽  
Thomas Blaschke ◽  
Bernhard Höfle
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser

An approach to classification of airborne laser scanning point cloud data in an urban environment

2011 ◽  
Vol 32 (24) ◽  
pp. 9151-9169 ◽  
Author(s):  
Cici Alexander ◽  
Kevin Tansey ◽  
Jörg Kaduk ◽  
David Holland ◽  
Nicholas J. Tate
Keyword(s):  
Urban Environment ◽  
Point Cloud ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Airborne Laser

scholarly journals CLASSIFICATION OF AIRBORNE LASER SCANNING DATA USING GEOMETRIC MULTI-SCALE FEATURES AND DIFFERENT NEIGHBOURHOOD TYPES

2016 ◽  
Vol III-3 ◽  
pp. 169-176 ◽  
Author(s):  
R. Blomley ◽  
B. Jutzi ◽  
M. Weinmann
Keyword(s):  
Feature Extraction ◽  
Laser Scanning ◽  
Benchmark Dataset ◽  
Airborne Laser Scanning ◽  
Geometric Features ◽  
Multi Scale ◽  
Airborne Laser ◽  
Single Scale ◽  
Detailed Evaluation

In this paper, we address the classification of airborne laser scanning data. We present a novel methodology relying on the use of complementary types of geometric features extracted from multiple local neighbourhoods of different scale and type. To demonstrate the performance of our methodology, we present results of a detailed evaluation on a standard benchmark dataset and we show that the consideration of multi-scale, multi-type neighbourhoods as the basis for feature extraction leads to improved classification results in comparison to single-scale neighbourhoods as well as in comparison to multi-scale neighbourhoods of the same type.

scholarly journals Automated Classification of Airborne Laser Scanning Point Clouds

2014 ◽  
pp. 269-292 ◽  
Author(s):  
Christoph Waldhauser ◽  
Ronald Hochreiter ◽  
Johannes Otepka ◽  
Norbert Pfeifer ◽  
Sajid Ghuffar ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Automated Classification ◽  
Airborne Laser
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