Novel fuzzy uncertainty modeling for land cover classification based on clustering analysis
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摘要
It is well known that there is a degree of fuzzy uncertainty in land cover classification using remote sensing (RS) images. In this article, we propose a novel fuzzy uncertainty modeling algorithm for representing the features of land cover patterns, and present an adaptive interval type-2 fuzzy clustering method. The proposed fuzzy uncertainty modeling method is performed in two main phases. First, the segmentation units of the input multi-spectral RS image data are subjected to objectbased interval-valued symbolic modeling. As a result, features for each land cover type are represented in the form of an intervalvalued symbolic vector, which describes the intra-class uncertainty better than the source data and improves the separability between different classes. Second, interval type-2 fuzzy sets are generated for each cluster based on the distance metric of the interval-valued vectors. This step characterizes the inter-class high-order fuzzy uncertainty and improves the classification accuracy. To demonstrate the advantages and effectiveness of the proposed approach, extensive experiments are conducted on two multispectral RS image datasets from regions with complex land cover characteristics, and the results are compared with those given by well-known fuzzy and conventional clustering algorithms.
It is well known that there is a degree of fuzzy uncertainty in land cover classification using remote sensing (RS) images. In this article, we propose a novel fuzzy uncertainty modeling algorithm for representing the features of land cover patterns, and present an adaptive interval type-2 fuzzy clustering method. The proposed fuzzy uncertainty modeling method is performed in two main phases. First, the segmentation units of the input multi-spectral RS image data are subjected to objectbased interval-valued symbolic modeling. As a result, features for each land cover type are represented in the form of an intervalvalued symbolic vector, which describes the intra-class uncertainty better than the source data and improves the separability between different classes. Second, interval type-2 fuzzy sets are generated for each cluster based on the distance metric of the interval-valued vectors. This step characterizes the inter-class high-order fuzzy uncertainty and improves the classification accuracy. To demonstrate the advantages and effectiveness of the proposed approach, extensive experiments are conducted on two multispectral RS image datasets from regions with complex land cover characteristics, and the results are compared with those given by well-known fuzzy and conventional clustering algorithms.
It is well known that there is a degree of fuzzy uncertainty in land cover classification using remote sensing (RS) images. In this article, we propose a novel fuzzy uncertainty modeling algorithm for representing the features of land cover patterns, and present an adaptive interval type-2 fuzzy clustering method. The proposed fuzzy uncertainty modeling method is performed in two main phases. First, the segmentation units of the input multi-spectral RS image data are subjected to objectbased interval-valued symbolic modeling. As a result, features for each land cover type are represented in the form of an intervalvalued symbolic vector, which describes the intra-class uncertainty better than the source data and improves the separability between different classes. Second, interval type-2 fuzzy sets are generated for each cluster based on the distance metric of the interval-valued vectors. This step characterizes the inter-class high-order fuzzy uncertainty and improves the classification accuracy. To demonstrate the advantages and effectiveness of the proposed approach, extensive experiments are conducted on two multispectral RS image datasets from regions with complex land cover characteristics, and the results are compared with those given by well-known fuzzy and conventional clustering algorithms.
引文
Ahmed M N,Yamany S M,Mohamed N,Farag A A,Moriarty T.2002.Amodified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data.IEEE Trans Med Imag,21:193-199
Cai W,Chen S,Zhang D.2007.Fast and robust fuzzy C-means clustering algorithms incorporating local information for image segmentation Pattern Recognit,40:825-838
Cheng J C,Guo H D,Shi W Z.2004.The Uncertainty of Remote Sensing Data.Beijing:Science Press
Chuang K S,Tzeng H L,Chen S,Wu J,Chen T J.2006.Fuzzy C-means clustering with spatial information for image segmentation.Compu Med Imag Grap,30:9-15
de Carvalho F A T,Lechevallier Y.2009.Dynamic clustering of intervalvalued data based on adaptive quadratic distances.IEEE Trans Sys Man Cybern A,39:1295-1306
de Carvalho F A T,Tenório C P.2010.Fuzzy K-means clustering algorithms for interval-valued data based on adaptive quadratic distances Fuzzy Sets Syst,161:2978-2999
Dias S,Brito P.2016.Off the beaten track:A new linear model for interva data.Eur J Oper Res,258:1118-1130
Dietterich T G.1998.Approximate statistical tests for comparing supervised classification learning algorithms.Neural Comput,10:1895-1923
D’Urso P.2017.Informational Paradigm,management of uncertainty and theoretical formalisms in the clustering framework:A review.Inf Sci400-401:30-62
Fisher P F.2010.Remote sensing of land cover classes as type 2 fuzzy sets Remote Sens Environ,114:309-321
Greenfield S,Chiclana F.2013.Accuracy and complexity evaluation of defuzzification strategies for the discretised interval type-2 fuzzy set.In J Approx Reason,54:1013-1033
Guru D S,Manjunatha K S,Manjunath S,Somashekara M T.2017.Interval valued symbolic representation of writer dependent features for online signature verification.Expert Syst Appl,80:232-243
He H,Liang T,Hu D,Yu X.2016a.Remote sensing clustering analysis based on object-based interval modeling.Comput Geosci,94:131-139
He H,Yu X C,Hu D.2016b.Land cover classification based on adaptive interval-valued type-2 fuzzy clustering analysis.Chin J Geol,712-720
Herman P A,Prasad G,McGinnity T M.2017.Designing an interval type-2fuzzy logic system for handling uncertainty effects in brain-computer interface classification of motor imagery induced EEG patterns.IEEETrans Fuzzy Syst,25:29-42
Hwang C,Rhee F C H.2007.Uncertain fuzzy clustering:Interval type-2fuzzy approach to C-means.IEEE Trans Fuzzy Syst,15:107-120
Irpino A,Verde R.2008.Dynamic clustering of interval data using a Wasserstein-based distance.Pattern Recognit Lett,29:1648-1658
Javadian M,Bagheri Shouraki S,Sheikhpour Kourabbaslou S.2017.Anovel density-based fuzzy clustering algorithm for low dimensiona feature space.Fuzzy Sets Syst,318:34-55
Li B,Zhao H,Lv Z H.2010.Parallel ISODATA clustering of remote sensing images based on MapReduce.In:International Conference on Web Information Systems and Mining.Huangshan.162-170
Li H X,Miao Z H,Lee E S.2002.Variable universe stable adaptive fuzzy control of a nonlinear system.Comput Math Appl,44:799-815
Lv Z,Hu Y,Zhong H,Wu J,Li B,Zhao H.2010.Parallel K-means clustering of remote sensing images based on MapReduce.Lect Notes in Comput Sci,6318:162-170
Long T N,Nguyen D D.2012.Land cover classification using interva type-2 fuzzy clustering for multi-spectral satellite imagery.In:IEEEInternational Conference on Systems,Man,and Cybernetics(SMC)Seoul 2371-2376
Long T N,Mai D S,Pedrycz W.2015.Semi-supervising interval type-2fuzzy C-means clustering with spatial information for multi-spectra satellite image classification and change detection.Comput Geosci2015,83:1-16
Lucas L A,Centeno T M,Delgado M R.2008.Land cover classification based on general type-2 fuzzy classifiers.Int J Fuzz Syst,10:207-216
Ma A,Zhong Y,Zhang L.2015.Adaptive multiobjective memetic fuzzy clustering algorithm for remote sensing imagery.IEEE Trans Geosci Remote Sens,53:4202-4217
Mai S D,Long T N.2015.Interval type-2 fuzzy C-means clustering with spatial information for land-cover classification.In:Asian Conference on Intelligent Information and Database Systems.Springer Int Publishing.387-397
Mendel J M.2013.On KM algorithms for solving type-2 fuzzy set problems.IEEE Trans Fuzzy Syst,21:426-446
Mendel J M,John R I,Liu F.2006.Interval type-2 fuzzy logic systems made simple.IEEE Trans Fuzzy Syst,14:808-821
Nie M,Tan W W.2008.Towards an efficient type reduction method for interval type-2 fuzzy logic system.In:IEEE World Congress on Computational Intelligence,Hong Kong.1425-1432
Moore R E.1966.Interval Analysis.Englewood Cliffs:Prentice-Hall
Nilashi M,Ibrahim O,Ahmadi H,Shahmoradi L.2017.A knowledgebased system for breast cancer classification using fuzzy logic method.Telemat Inf,34:133-144
Nguyen D D,Ngo L T,Pham L T,Pedrycz W.2015.Towards hybrid clustering approach to data classification:Multiple kernels based interval-valued fuzzy C-means algorithms.Fuzzy Sets Syst,279:17-39
Rocchini D,Foody G M,Nagendra H,Ricotta C,Anand M,He K S,Amici V,Kleinschmit B,F?rster M,Schmidtlein S,Feilhauer H,Ghisla A,Metz M,Neteler M.2013.Uncertainty in ecosystem mapping by remote sensing.Comput Geosci,50:128-135
Ramos-Guajardo A B,Grzegorzewski P.2016.Distance-based linear discriminant analysis for interval-valued data.Inf Sci,372:591-607
Lopes S C,Pumi G,Zaniol K.2013.Mallows Distance in VARFIMA(0,d,0)Processes.Commun Stat-Simul Comput,42:24-51
Tran L,Duckstein L.2002.Comparison of fuzzy numbers using a fuzzy distance measure.Fuzzy Sets Syst,130:331-341
Tenenbaum D E,Yang Y,Zhou W.2011.A comparison of object-oriented image classification and transect sampling methods for obtaining land cover information from digital orthophotography.Gisci Remote Sens,48:112-129
Tizhoosh H R.2005.Image thresholding using type II fuzzy sets.Pattern Recognit,38:2363-2372
Wagner C,Miller S,Garibaldi J M,Anderson D T,Havens T C.2015.From interval-valued data to general type-2 fuzzy sets.IEEE Trans Fuzzy Syst,23:248-269
Wu D,Tan W W.2005.Computationally efficient type-reduction strategies for a type-2 fuzzy logic controller.In:The 14th IEEE International Conference on Fuzzy Systems,2005.FUZZ'05.Reno.353-358
Wu H,Mendel J M.2002.Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems.IEEE Transactions on Fuzzy Systems,10:622-639
Xia Y,Feng D D,Wang T,Zhao R,Zhang Y.2007.Image segmentation by clustering of spatial patterns.Pattern Recognition Lett,28:1548-1555
Xie J,Zhang X.2012.Clustering of hyper spectral image based on improved fuzzy C means algorithm.J Converg Inf Tech,7:320-327
Xu M,Watanachaturaporn P,Varshney P K,Arora M.2005.Decision tree regression for soft classification of remote sensing data.Remote Sens Environ,97:322-336
Yu X C,An W J,He H.2012.A method of auto classification based on object oriented unsupervised classification.Progress Geophys,27:744-749
Yu X C,He H,Hu D,Zhou W.2014.Land cover classification of remote sensing imagery based on interval-valued data fuzzy C-means algorithm.Sci China Earth Sci,57:1306-1313
Zadeh L A.1975.The concept of a linguistic variable and its application to approximate reasoning-I.Inf Sci,8:199-249
Zeng J,Liu Z Q.2007.Type-2 Fuzzy Sets for Pattern Classification:AReview.In:IEEE Symposium on Foundations of Computational Intelligence,2007.FOCI 2007.Honolulu.193-200
Zhong Y,Zhang S,Zhang L.2013.Automatic fuzzy clustering based on adaptive multi-objective differential evolution for remote sensing imagery.IEEE J Sel Top Appl Earth Observations Remote Sens,6:2290-2301
Zhong Y,Ma A,Zhang L.2014.An adaptive memetic fuzzy clustering algorithm with spatial information for remote sensing imagery.IEEE JSel Top Appl Earth Observations Remote Sens,7:1235-1248
Zhou D,Li J,Zha H.2005.A new Mallows distance based metric for comparing clusterings.In:Proceedings of the Twenty-Second International Conference(ICML 2005).Bonn.1028-1035
Cai W,Chen S,Zhang D.2007.Fast and robust fuzzy C-means clustering algorithms incorporating local information for image segmentation Pattern Recognit,40:825-838
Cheng J C,Guo H D,Shi W Z.2004.The Uncertainty of Remote Sensing Data.Beijing:Science Press
Chuang K S,Tzeng H L,Chen S,Wu J,Chen T J.2006.Fuzzy C-means clustering with spatial information for image segmentation.Compu Med Imag Grap,30:9-15
de Carvalho F A T,Lechevallier Y.2009.Dynamic clustering of intervalvalued data based on adaptive quadratic distances.IEEE Trans Sys Man Cybern A,39:1295-1306
de Carvalho F A T,Tenório C P.2010.Fuzzy K-means clustering algorithms for interval-valued data based on adaptive quadratic distances Fuzzy Sets Syst,161:2978-2999
Dias S,Brito P.2016.Off the beaten track:A new linear model for interva data.Eur J Oper Res,258:1118-1130
Dietterich T G.1998.Approximate statistical tests for comparing supervised classification learning algorithms.Neural Comput,10:1895-1923
D’Urso P.2017.Informational Paradigm,management of uncertainty and theoretical formalisms in the clustering framework:A review.Inf Sci400-401:30-62
Fisher P F.2010.Remote sensing of land cover classes as type 2 fuzzy sets Remote Sens Environ,114:309-321
Greenfield S,Chiclana F.2013.Accuracy and complexity evaluation of defuzzification strategies for the discretised interval type-2 fuzzy set.In J Approx Reason,54:1013-1033
Guru D S,Manjunatha K S,Manjunath S,Somashekara M T.2017.Interval valued symbolic representation of writer dependent features for online signature verification.Expert Syst Appl,80:232-243
He H,Liang T,Hu D,Yu X.2016a.Remote sensing clustering analysis based on object-based interval modeling.Comput Geosci,94:131-139
He H,Yu X C,Hu D.2016b.Land cover classification based on adaptive interval-valued type-2 fuzzy clustering analysis.Chin J Geol,712-720
Herman P A,Prasad G,McGinnity T M.2017.Designing an interval type-2fuzzy logic system for handling uncertainty effects in brain-computer interface classification of motor imagery induced EEG patterns.IEEETrans Fuzzy Syst,25:29-42
Hwang C,Rhee F C H.2007.Uncertain fuzzy clustering:Interval type-2fuzzy approach to C-means.IEEE Trans Fuzzy Syst,15:107-120
Irpino A,Verde R.2008.Dynamic clustering of interval data using a Wasserstein-based distance.Pattern Recognit Lett,29:1648-1658
Javadian M,Bagheri Shouraki S,Sheikhpour Kourabbaslou S.2017.Anovel density-based fuzzy clustering algorithm for low dimensiona feature space.Fuzzy Sets Syst,318:34-55
Li B,Zhao H,Lv Z H.2010.Parallel ISODATA clustering of remote sensing images based on MapReduce.In:International Conference on Web Information Systems and Mining.Huangshan.162-170
Li H X,Miao Z H,Lee E S.2002.Variable universe stable adaptive fuzzy control of a nonlinear system.Comput Math Appl,44:799-815
Lv Z,Hu Y,Zhong H,Wu J,Li B,Zhao H.2010.Parallel K-means clustering of remote sensing images based on MapReduce.Lect Notes in Comput Sci,6318:162-170
Long T N,Nguyen D D.2012.Land cover classification using interva type-2 fuzzy clustering for multi-spectral satellite imagery.In:IEEEInternational Conference on Systems,Man,and Cybernetics(SMC)Seoul 2371-2376
Long T N,Mai D S,Pedrycz W.2015.Semi-supervising interval type-2fuzzy C-means clustering with spatial information for multi-spectra satellite image classification and change detection.Comput Geosci2015,83:1-16
Lucas L A,Centeno T M,Delgado M R.2008.Land cover classification based on general type-2 fuzzy classifiers.Int J Fuzz Syst,10:207-216
Ma A,Zhong Y,Zhang L.2015.Adaptive multiobjective memetic fuzzy clustering algorithm for remote sensing imagery.IEEE Trans Geosci Remote Sens,53:4202-4217
Mai S D,Long T N.2015.Interval type-2 fuzzy C-means clustering with spatial information for land-cover classification.In:Asian Conference on Intelligent Information and Database Systems.Springer Int Publishing.387-397
Mendel J M.2013.On KM algorithms for solving type-2 fuzzy set problems.IEEE Trans Fuzzy Syst,21:426-446
Mendel J M,John R I,Liu F.2006.Interval type-2 fuzzy logic systems made simple.IEEE Trans Fuzzy Syst,14:808-821
Nie M,Tan W W.2008.Towards an efficient type reduction method for interval type-2 fuzzy logic system.In:IEEE World Congress on Computational Intelligence,Hong Kong.1425-1432
Moore R E.1966.Interval Analysis.Englewood Cliffs:Prentice-Hall
Nilashi M,Ibrahim O,Ahmadi H,Shahmoradi L.2017.A knowledgebased system for breast cancer classification using fuzzy logic method.Telemat Inf,34:133-144
Nguyen D D,Ngo L T,Pham L T,Pedrycz W.2015.Towards hybrid clustering approach to data classification:Multiple kernels based interval-valued fuzzy C-means algorithms.Fuzzy Sets Syst,279:17-39
Rocchini D,Foody G M,Nagendra H,Ricotta C,Anand M,He K S,Amici V,Kleinschmit B,F?rster M,Schmidtlein S,Feilhauer H,Ghisla A,Metz M,Neteler M.2013.Uncertainty in ecosystem mapping by remote sensing.Comput Geosci,50:128-135
Ramos-Guajardo A B,Grzegorzewski P.2016.Distance-based linear discriminant analysis for interval-valued data.Inf Sci,372:591-607
Lopes S C,Pumi G,Zaniol K.2013.Mallows Distance in VARFIMA(0,d,0)Processes.Commun Stat-Simul Comput,42:24-51
Tran L,Duckstein L.2002.Comparison of fuzzy numbers using a fuzzy distance measure.Fuzzy Sets Syst,130:331-341
Tenenbaum D E,Yang Y,Zhou W.2011.A comparison of object-oriented image classification and transect sampling methods for obtaining land cover information from digital orthophotography.Gisci Remote Sens,48:112-129
Tizhoosh H R.2005.Image thresholding using type II fuzzy sets.Pattern Recognit,38:2363-2372
Wagner C,Miller S,Garibaldi J M,Anderson D T,Havens T C.2015.From interval-valued data to general type-2 fuzzy sets.IEEE Trans Fuzzy Syst,23:248-269
Wu D,Tan W W.2005.Computationally efficient type-reduction strategies for a type-2 fuzzy logic controller.In:The 14th IEEE International Conference on Fuzzy Systems,2005.FUZZ'05.Reno.353-358
Wu H,Mendel J M.2002.Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems.IEEE Transactions on Fuzzy Systems,10:622-639
Xia Y,Feng D D,Wang T,Zhao R,Zhang Y.2007.Image segmentation by clustering of spatial patterns.Pattern Recognition Lett,28:1548-1555
Xie J,Zhang X.2012.Clustering of hyper spectral image based on improved fuzzy C means algorithm.J Converg Inf Tech,7:320-327
Xu M,Watanachaturaporn P,Varshney P K,Arora M.2005.Decision tree regression for soft classification of remote sensing data.Remote Sens Environ,97:322-336
Yu X C,An W J,He H.2012.A method of auto classification based on object oriented unsupervised classification.Progress Geophys,27:744-749
Yu X C,He H,Hu D,Zhou W.2014.Land cover classification of remote sensing imagery based on interval-valued data fuzzy C-means algorithm.Sci China Earth Sci,57:1306-1313
Zadeh L A.1975.The concept of a linguistic variable and its application to approximate reasoning-I.Inf Sci,8:199-249
Zeng J,Liu Z Q.2007.Type-2 Fuzzy Sets for Pattern Classification:AReview.In:IEEE Symposium on Foundations of Computational Intelligence,2007.FOCI 2007.Honolulu.193-200
Zhong Y,Zhang S,Zhang L.2013.Automatic fuzzy clustering based on adaptive multi-objective differential evolution for remote sensing imagery.IEEE J Sel Top Appl Earth Observations Remote Sens,6:2290-2301
Zhong Y,Ma A,Zhang L.2014.An adaptive memetic fuzzy clustering algorithm with spatial information for remote sensing imagery.IEEE JSel Top Appl Earth Observations Remote Sens,7:1235-1248
Zhou D,Li J,Zha H.2005.A new Mallows distance based metric for comparing clusterings.In:Proceedings of the Twenty-Second International Conference(ICML 2005).Bonn.1028-1035