Article : Remote Sensing Data Binary Classification Using Boosting with Simple Classifiers
Authors : Białecki, M.Institute of Geophysics, Polish Academy of Sciences, Warszawa, Poland, email@example.com, Logvinov, I. M.Institute of Geophysics, National Academy of Sciences of Ukraine, Kiev, Ukraine, firstname.lastname@example.org, Cichy, A.AGH University of Science and Technology, Department of Geology, Geophysics and Environmental Protection, Kraków, Poland, email@example.com, Ossowski, A.AGH University of Science and Technology, Department of Geology, Geophysics and Environmental Protection, Kraków, Poland, firstname.lastname@example.org, 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, email@example.com, Vujić, E.Faculty of Geodesy, University of Zagreb, Zagreb, Croatia, firstname.lastname@example.org, Bała, M.AGH University of Science and Technology, Department of Geology, Geophysics and Environmental Protection, Kraków, Poland, email@example.com, Cichy, A.AGH University of Science and Technology, Department of Geology, Geophysics and Environmental Protection, Kraków, Poland, firstname.lastname@example.org, Nowakowski, A.Space Research Centre, Polish Academy of Sciences, Warszawa, Poland, email@example.com,
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.
Bibliography : 1 Benediktsson, J.A., J. Chanussot, and M. Fauvel (2007), Multiple classifier systems in remote sensing: from basics to recent developments. In: M. Haindl, J. Kittler, and F. Roli (eds.), Multiple Classifier Systems, Lecture Notes in Computer Science, Vol. 4472, Springer, Berlin Heidelberg, 501-512, DOI: 10.1007/978-3-540-72523-7_50.
2 Briem, G.J., J.A. Benediktsson, and J.R. Sveinsson (2002), Multiple classifiers applied to multisource remote sensing data, IEEE Trans. Geosci. Remote Sens. 40, 10, 2291-2299, DOI: 10.1109/TGRS.2002.802476.
3 Brito, P.L., and J.A. Quintanilha (2012), A literature review, 2001-2008, of classification methods and inner urban characteristics identified in multispectral remote sensing images. In: Proc. 4th GEOBIA, 7-9 May 2012, Rio de Janeiro, Brazil, 586-591.
4 Cerquides, J., M. López-Sánchez, S. Ontañón, E. Puertas, A. Puig, O. Pujol, and D. Tost (2006), Classification algorithms for biomedical volume datasets. In: R. Marin, E. Onaindia, A. Bugarin, and J. Santos (eds.), Current Topics in Artificial Intelligence, Springer, Berlin Heidelberg, 143-152, DOI: 10.1007/11881216_16.
5 Chan, J.C.W., and D. Paelinckx (2008), Evaluation of Random Forest and AdaBoost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery, Remote Sens. Environ. 112, 6, 2999-3011, DOI: 10.1016/j.rse.2008.02.011.
6 Dlagnekov, L. (2004), License Plate Detection using AdaBoost, Computer Science and Engineering Department, University of California, San Diego, USA.
7 Freund, Y., and R.E. Schapire (1996), Experiments with a new boosting algorithm. In: Proc. 13th Int. Conf. Machine Learning, 3-6 July 1996, Bari, Italy, 148-156.
8 Friedl, M.A., C.E. Brodley, and A.H. Strahler (1999), Maximizing land cover classification accuracies produced by decision trees at continental to global scales, IEEE Trans. Geosci. Remote Sens. 37, 2, 969-977, DOI: 10.1109/36.752215.
9 Friedl, M.A., D.K. McIver, J.C.F. Hodges, X.Y. Zhang, D. Muchoney, A.H. Strahler, C.E. Woodcock, S. Gopal, A. Schneider, A. Cooper, A. Baccini, F. Gao, and C. Schaaf (2002), Global land cover mapping from MODIS: algorithms and early results, Remote Sens. Environ. 83, 1-2, 287-302, DOI: 10.1016/S0034-4257(02)00078-0.
10 Holte, R.C. (1993), Very simple classification rules perform well on most commonly used datasets, Mach. Learn. 11, 1, 63-91, DOI: 10.1023/A:1022631118932.
11 Kearns, M. (1988), Thoughts on hypothesis boosting, University of Pennsylvania, Machine Learning class project, 105 pp. (unpublished).
12 Lawrence, R., A. Bunn, S. Powell, and M. Zambon (2004), Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis, Remote Sens. Environ. 90, 3, 331-336, DOI: 10.1016/j.rse.2004.01.007.
13 Matas, J., and J. Sochman (2001), Adaboost. Center for Machine Perception, Czech Technical University, Prague, Czech Republic.
14 McIver, D.K., and M.A. Friedl (2001), Estimating pixel-scale land cover classification confidence using nonparametric machine learning methods, IEEE Trans. Geosci. Remote Sens. 39, 9, 1959-1968, DOI: 10.1109/36.951086.
15 Quinlan, J.R. (1996), Bagging, boosting, and C4.5. In: Proc. 13th National Conference on Artificial Intelligence, 12-17 February 1996, Phoenix, USA, Vol. 1, 725-730. Schneider, A., M.A. Friedl, and D. Potere (2010), Mapping global urban areas using MODIS 500-m data: New methods and datasets based on ‘urban ecoregions’, Remote Sens. Environ. 114, 8, 1733-1746, DOI: 10.1016/j.rse.2010.03.003.
16 Valiant, L.G. (1984), A theory of the learnable, Commun. ACM 27, 11, 1134-1142, DOI: 10.1145/1968.1972.
17 Viola, P., and M. Jones (2001), Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition CVPR 2001, Vol. 1, I-511-I-518, DOI: 10.1109/CVPR.2001.990517.
18 Viola, P., and M.J. Jones (2004), Robust real-time face detection, Int. J. Comput. Vision 57, 2, 137-154, DOI: 10.1023/B:VISI.0000013087.49260.fb.
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