A Supervised Object-Based Detection of Landslides and Man-Made Slopes Using Airborne Laser Scanning Data

2017 ◽  
pp. 23-50
Author(s):  
Biswajeet Pradhan ◽  
Ali Alsaleh
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Object Based

scholarly journals Classification of tree species composition using a combination of multispectral imagery and airborne laser scanning data

2017 ◽  
Vol 63 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Maroš Sedliak ◽  
Ivan Sačkov ◽  
Ladislav Kulla
Keyword(s):  
Species Composition ◽  
Tree Species ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Tree Species Composition ◽  
Object Based Image Analysis ◽  
Airborne Laser ◽  
Object Based ◽  
Individual Trees

AbstractRemote Sensing provides a variety of data and resources useful in mapping of forest. Currently, one of the common applications in forestry is the identification of individual trees and tree species composition, using the object-based image analysis, resulting from the classification of aerial or satellite imagery. In the paper, there is presented an approach to the identification of group of tree species (deciduous - coniferous trees) in diverse structures of close-to-nature mixed forests of beech, fir and spruce managed by selective cutting. There is applied the object-oriented classification based on multispectral images with and without the combination with airborne laser scanning data in the eCognition Developer 9 software. In accordance to the comparison of classification results, the using of the airborne laser scanning data allowed identifying ground of terrain and the overall accuracy of classification increased from 84.14% to 87.42%. Classification accuracy of class “coniferous” increased from 82.93% to 85.73% and accuracy of class “deciduous” increased from 84.79% to 90.16%.

Using object-based analysis to derive surface complexity information for improved filtering of airborne laser scanning data

2017 ◽  
Vol 11 (1) ◽  
pp. 11-19
Author(s):  
Menglong Yan ◽  
Thomas Blaschke ◽  
Hongzhao Tang ◽  
Chenchao Xiao ◽  
Xian Sun ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Object Based

An object-based analysis filtering algorithm for airborne laser scanning

2012 ◽  
Vol 33 (22) ◽  
pp. 7099-7116 ◽  
Author(s):  
Menglong Yan ◽  
Thomas Blaschke ◽  
Yu Liu ◽  
Lun Wu
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Filtering Algorithm ◽  
Airborne Laser ◽  
Object Based

scholarly journals Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification

Sensors ◽  
2008 ◽  
Vol 8 (8) ◽  
pp. 4505-4528 ◽  
Author(s):  
Martin Rutzinger ◽  
Bernhard Höfle ◽  
Markus Hollaus ◽  
Norbert Pfeifer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Vegetation Classification ◽  
Airborne Laser Scanning ◽  
Urban Vegetation ◽  
Full Waveform ◽  
Airborne Laser ◽  
Object Based ◽  
Point Cloud Analysis ◽  
Cloud Analysis

scholarly journals CHANGE DETECTION BETWEEN DIGITAL SURFACE MODELS FROM AIRBORNE LASER SCANNING AND DENSE IMAGE MATCHING USING CONVOLUTIONAL NEURAL NETWORKS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 453-460 ◽  
Author(s):  
Z. Zhang ◽  
G. Vosselman ◽  
M. Gerke ◽  
C. Persello ◽  
D. Tuia ◽  
...  
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Point Clouds ◽  
Recall Rate ◽  
Point Of View ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Object Based ◽  
Dense Image ◽  
Different Characteristics

<p><strong>Abstract.</strong> Airborne photogrammetry and airborne laser scanning are two commonly used technologies used for topographical data acquisition at the city level. Change detection between airborne laser scanning data and photogrammetric data is challenging since the two point clouds show different characteristics. After comparing the two types of point clouds, this paper proposes a feed-forward Convolutional Neural Network (CNN) to detect building changes between them. The motivation from an application point of view is that the multimodal point clouds might be available for different epochs. Our method contains three steps: First, the point clouds and orthoimages are converted to raster images. Second, square patches are cropped from raster images and then fed into CNN for change detection. Finally, the original change map is post-processed with a simple connected component analysis. Experimental results show that the patch-based recall rate reaches 0.8146 and the precision rate reaches 0.7632. Object-based evaluation shows that 74 out of 86 building changes are correctly detected.</p>

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