DocumentCode :
1893808
Title :
Import vector machines based classification of multisensor remote sensing data
Author :
Waske, Björn ; Roscher, Ribana ; Klemenjak, Sascha
Author_Institution :
Inst. of Geodesy & Geoinf., Univ. of Bonn, Bonn, Germany
fYear :
2011
fDate :
24-29 July 2011
Firstpage :
2931
Lastpage :
2934
Abstract :
The classification of multisensor data sets, consisting of multitemporal SAR data and multispectral is addressed. In the present study, Import Vector Machines (IVM) are applied on two data sets, consisting of (i) Envisat ASAR/ERS-2 SAR data and a Landsat 5 TM scene, and (h) TerraSAR-X data and a RapidEye scene. The performance of IVM for classifying multisensor data is evaluated and the method is compared to Support Vector Machines (SVM) in terms of accuracy and complexity. In general, the experimental results demonstrate that the classification accuracy is improved by the multisensor data set. Moreover, IVM and SVM perform similar in terms of the classification accuracy. However, the number of import vectors is considerably less than the number of support vectors, and thus the computation time of the IVM classification is lower. IVM can directly be applied to the multi-class problems and provide probabilistic outputs. Overall IVM constitutes a feasible method and alternative to SVM.
Keywords :
geophysical image processing; image classification; knowledge engineering; radar imaging; remote sensing by radar; spaceborne radar; synthetic aperture radar; Envisat ASAR ERS-2 SAR data; IVM; Landsat 5 TM scene; RapidEye scene; SVM comparison; TerraSAR-X data; computation time; data classification; import vector machines; multisensor remote sensing data; multispectral data; multitemporal SAR data; support vector machines; Accuracy; Agriculture; Kernel; Logistics; Remote sensing; Support vector machine classification; Import Vector Machines; SAR; Support Vector Machines; land cover classification; multispectral;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International
Conference_Location :
Vancouver, BC
ISSN :
2153-6996
Print_ISBN :
978-1-4577-1003-2
Type :
conf
DOI :
10.1109/IGARSS.2011.6049829
Filename :
6049829
Link To Document :
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