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Fusion of imaging spectrometer LIDAR data using support vector machines for land cover classification


Koetz, Benjamin; Morsdorf, Felix; Curt, T; van der Linden, S; Borgniet, L; Odermatt, Daniel; Alleaume, S; Lampin, C; Jappiot, C; Allgöwer, Britta (2007). Fusion of imaging spectrometer LIDAR data using support vector machines for land cover classification. In: ISPRS Working Group VII/1 Workshop ISPMSRS'07: "Physical Measurements and Signatures in Remote Sensing" , Davos (CH), 12 March 2007 - 14 March 2007, 297-301.

Abstract

A combination of the two remote sensing systems, imaging spectrometry (IS) and Light Detection And Ranging (LiDAR), is well suited to map fuel types, especially within the complex wildland urban interface. LiDAR observations sample the spatial information dimension describing geometric surface properties. Imaging spectrometry on the other hand samples the spectral dimension, which is sensitive for discrimination of species and surface types. As a non-parametric classifier Support Vector Machines (SVM) are particularly well adapted to classify data of high dimensionality and from multiple-sources as proposed in this work. The presented approach achieves an improved land cover mapping based on a single SVM classifier combining the spectral and spatial information dimensions provided by imaging spectrometry and LiDAR.

Abstract

A combination of the two remote sensing systems, imaging spectrometry (IS) and Light Detection And Ranging (LiDAR), is well suited to map fuel types, especially within the complex wildland urban interface. LiDAR observations sample the spatial information dimension describing geometric surface properties. Imaging spectrometry on the other hand samples the spectral dimension, which is sensitive for discrimination of species and surface types. As a non-parametric classifier Support Vector Machines (SVM) are particularly well adapted to classify data of high dimensionality and from multiple-sources as proposed in this work. The presented approach achieves an improved land cover mapping based on a single SVM classifier combining the spectral and spatial information dimensions provided by imaging spectrometry and LiDAR.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:14 March 2007
Deposited On:08 May 2013 12:32
Last Modified:06 Apr 2017 13:04
Publisher:ISPRS
Number:XXXVI7/C50
Free access at:Official URL. An embargo period may apply.
Official URL:http://www.isprs.org/proceedings/xxxvi/7-c50/papers/P29.pdf
Related URLs:http://www.isprs.org/proceedings/xxxvi/7-c50/ (Publisher)

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