Article : Remote Sensing Data Binary Classification Using Boosting with Simple Classifiers
Authors : Białecki, M.Institute of Geophysics, Polish Academy of Sciences, Warszawa, Poland, firstname.lastname@example.org, Logvinov, I. M.Institute of Geophysics, National Academy of Sciences of Ukraine, Kiev, Ukraine, email@example.com, Cichy, A.AGH University of Science and Technology, Department of Geology, Geophysics and Environmental Protection, Kraków, Poland, firstname.lastname@example.org, Ossowski, A.AGH University of Science and Technology, Department of Geology, Geophysics and Environmental Protection, Kraków, Poland, email@example.com, Zhao, N.State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (CDUT), Chengdu, China, Li, R.Xi’an Technological University, Computer Science and Engineering College, Xi’an, China, firstname.lastname@example.org, Vujić, E.Faculty of Geodesy, University of Zagreb, Zagreb, Croatia, email@example.com, Bała, M.AGH University of Science and Technology, Department of Geology, Geophysics and Environmental Protection, Kraków, Poland, firstname.lastname@example.org, Cichy, A.AGH University of Science and Technology, Department of Geology, Geophysics and Environmental Protection, Kraków, Poland, email@example.com, Nowakowski, A.Space Research Centre, Polish Academy of Sciences, Warszawa, Poland, firstname.lastname@example.org,
Abstract : Boosting is a classification method which has been proven useful in non-satellite image processing while it is still new to satellite remote sensing. It is a meta-algorithm, which builds a strong classifier from many weak ones in iterative way. We adapt the AdaBoost.M1 boosting algorithm in a new land cover classification scenario based on utilization of very simple threshold classifiers employing spectral and contextual information. Thresholds for the classifiers are automatically calculated adaptively to data statistics. The proposed method is employed for the exemplary problem of artificial area identification. Classification of IKONOS multispectral data results in short computational time and overall accuracy of 94.4% comparing to 94.0% obtained by using AdaBoost.M1 with trees and 93.8% achieved using Random Forest. The influence of a manipulation of the final threshold of the strong classifier on classification results is reported.
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Qute : Białecki, M. ,Logvinov, I. M. ,Cichy, A. ,Ossowski, A. ,Zhao, N. ,Li, R. ,Vujić, E. ,Bała, M. ,Cichy, A. ,Nowakowski, A. ,Nowakowski, A. , Remote Sensing Data Binary Classification Using Boosting with Simple Classifiers. Acta Geophysica Vol. 63, no. 5/2015