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http://dx.doi.org/10.7780/kjrs.2011.27.4.445

Application of Bayesian Probability Rule to the Combination of Spectral and Temporal Contextual Information in Land-cover Classification  

Lee, Sang-Won (Dept. of Geoinformatic Engineering, Inha University)
Park, No-Wook (Dept. of Geoinformatic Engineering, Inha University)
Publication Information
Korean Journal of Remote Sensing / v.27, no.4, 2011 , pp. 445-455 More about this Journal
Abstract
A probabilistic classification framework is presented that can combine temporal contextual information derived from an existing land-cover map in order to improve the classification accuracy of land-cover classes that can not be discriminated well when using spectral information only. The transition probability is computed by using the existing land-cover map and training data, and considered as a priori probability. By combining the a priori probability with conditional probability computed from spectral information via a Bayesian combination rule, the a posteriori probability is finally computed and then the final land-cover types are determined. The method presented in this paper can be adopted to any probabilistic classification algorithms in a simple way, compared with conventional classification methods that require heavy computational loads to incorporate the temporal contextual information. A case study for crop classification using time-series MODIS data sets is carried out to illustrate the applicability of the presented method. The classification accuracies of the land-cover classes, which showed lower classification accuracies when using only spectral information due to the low resolution MODIS data, were much improved by combining the temporal contextual information. It is expected that the presented probabilistic method would be useful both for updating the existing past land-cover maps, and for improving the classification accuracy.
Keywords
Classification; temporal contextual information; crop; MODIS;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 Vapnik, V.N., 1995. The Nature of Statistical Learning Theory, Springer, New York, USA.
2 Yang, C.Z.X., 1998. Study of remote sensing image texture analysis and classification using wavelet, International Journal of Remote Sensing, 19(16): 3197-3203.   DOI   ScienceOn
3 Zhao, M., F.A. Heinsch, R.R. Nemani, and S.W. Running, 2005. Improvements of the MODIS terrestrial gross and net primary production global data set, Remote Sensing of Environment, 95(2): 164-176.   DOI   ScienceOn
4 Park, N.-W., 2010. Accounting for temporal contextual information in land-cover classification with multi-sensor SAR data, International Journal of Remote Sensing, 31(2): 281-298.   DOI   ScienceOn
5 Mathur, A. and G.M. Foody, 2008. Crop classification by support vector machine with intelligently selected training data for an operational application, International Journal of Remote Sensing, 29(8): 2227-2240.   DOI   ScienceOn
6 Swain, P.H., 1978. Bayesian classification in a time-varying environment, IEEE Transactions on Geoscience and Remote Sensing, 8(12): 880-883.
7 Oh, H.-J., N.-W. Park, S.-S. Lee, and S. Lee, 2011. Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping, International Journal of Remote Sensing, in press.
8 Pal, M. and P.M. Mather, 2005. Support vector machines for classification in remote sensing, International Journal of Remote Sensing, 26(5): 1007-1011.   DOI   ScienceOn
9 Melgani, F. and S.B. Serpico, 2003. A Markov random field approach to spatio-temporal contextual image classification, IEEE Transactions on Geoscience and Remote Sensing, 41(11): 2478-2487.   DOI   ScienceOn
10 Melgani, F. and L. Bruzzone, 2004. Classification of hyperspectral remote sensing images with support vector machines, IEEE Transactions on Geoscience and Remote Sensing, 42(8): 1778-1790.   DOI
11 박노욱, 지광훈, 2007. 타겟 분해 기반 특징과 확률비 모델을 이용한 다중 주파수 편광 SAR자료의 결정 수준 융합, 대한원격탐사학회지, 23(2): 89-101.   DOI
12 Melgani, F. and S.B. Serpico, 2002. A statistical approach to the fusion of spectral and spatiotemporal contextual information for the classification of remote-sensing images, Pattern Recognition Letters, 23(9): 1053-1061.   DOI   ScienceOn
13 Foody, G.M. and A. Mathur, 2004. A relative evaluation of multi class image classification by support vector machines, IEEE Transactions on Geoscience and Remote Sensing, 42(6): 1335-1343.   DOI
14 Johnson, D.M. and R. Mueller, 2010. The 2009 Cropland data layer, Photogrammetric Engineering & Remote Sensing, 76(11): 1201-1205.
15 Kalayeh, H.M. and D.A. Landgrebe, 1986. Utilizing multitemporal data by a stochastic model, IEEE Transactions on Geoscience and Remote Sensing, 24(5): 792-795.
16 Cristianini, N. and J. Shawe-Taylor, 2000. An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, UK.
17 Ehlers, M., M. Gahler, and R. Janowsky, 2003. Automated analysis of ultra high resolution remote sensing data for biotope type mapping: new possibilities and challenges, ISPRS Journal of Photogrammetry and Remote Sensing, 57(5-6): 315-326.   DOI   ScienceOn
18 Bruzzone, L. and S.B. Serpico, 1997. An iterative technique for the detection of land-cover transitions in multitemporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 35(4): 858-867.   DOI   ScienceOn
19 Camps-Valls, G. and L. Bruzzone, 2009. Kernel Methods for Remote Sensing Data Analysis, Wiley, Chichester, UK.
20 Chang C.-C. and C.-J. Lin, 2011. LIBSVM: a library for support vector machines, ACM Transactions on Intelligent Systems and Technology, 2(3): 1-27.
21 천기선, 박재국, 2007. 산사태 취약지에서의 토지피복 상태 변화추적, 한국지형공간정보학회지, 15(3):69-76.
22 이기원, 전소희, 권병두, 2005. GLCM/GLDV 기반 texture 알고리즘 구현과 고해상도 영상분석 적용, 대한원격탐사학회지, 21(2): 121-133.   DOI
23 이상훈, 2003. 퍼지 클래스 벡터를 이용하는 다중센서 융합에 의한 무감독 영상분류, 대한원격탐사학회지, 19(4): 329-339.   DOI
24 장동호, 2005. 고해상도 위성영상을 이용한 홍수 전, 후의 하도 내 퇴적환경 변화 탐지:강릉 사천천 사례연구, 한국지형학회지, 12(3): 49-58.
25 홍창희, 2009. 고해상도 영상자료 및 객체지향분류기법을 이용한 식생분류 정확도 향상 방안 연구, 한국환경영향평가학회지, 18(6): 387-392.
26 Bazi, Y. and F. Melgani, 2006. Toward an optimal SVM classification system for hyperspectral remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 44(11): 3374-3385.   DOI
27 김민호, 이충근, 박호기, 이재은, 구본철, 신진철, 2008. Landsat 위성영상을 이용한 벼 생육 및 수량 모니터링, 한국작물학회지, 53(4): 288-393.