Skip to main content
SpringerLink
Log in

Novel fuzzy uncertainty modeling for land cover classification based on clustering analysis

  • Research Paper
  • Published:
Science China Earth Sciences Aims and scope Submit manuscript

Abstract

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 object-based interval-valued symbolic modeling. As a result, features for each land cover type are represented in the form of an interval-valued 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Ahmed M N, Yamany S M, Mohamed N, Farag A A, Moriarty T. 2002. A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imag, 21: 193–199

    Google Scholar 

  • 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

    Google Scholar 

  • Cheng J C, Guo H D, Shi W Z. 2004. The Uncertainty of Remote Sensing Data. Beijing: Science Press

    Google Scholar 

  • Chuang K S, Tzeng H L, Chen S, Wu J, Chen T J. 2006. Fuzzy C-means clustering with spatial information for image segmentation. Comput Med Imag Grap, 30: 9–15

    Google Scholar 

  • de Carvalho F A T, Lechevallier Y. 2009. Dynamic clustering of intervalvalued data based on adaptive quadratic distances. IEEE Trans Syst Man Cybern A, 39: 1295–1306

    Google Scholar 

  • 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

    Google Scholar 

  • Dias S, Brito P. 2016. Off the beaten track: A new linear model for interval data. Eur J Oper Res, 258: 1118–1130

    Google Scholar 

  • Dietterich T G. 1998. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput, 10: 1895–1923

    Google Scholar 

  • D’Urso P. 2017. Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A review. Inf Sci, 400-401: 30–62

    Google Scholar 

  • Fisher P F. 2010. Remote sensing of land cover classes as type 2 fuzzy sets. Remote Sens Environ, 114: 309–321

    Google Scholar 

  • Greenfield S, Chiclana F. 2013. Accuracy and complexity evaluation of defuzzification strategies for the discretised interval type-2 fuzzy set. Int J Approx Reason, 54: 1013–1033

    Google Scholar 

  • 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

    Google Scholar 

  • He H, Liang T, Hu D, Yu X. 2016a. Remote sensing clustering analysis based on object-based interval modeling. Comput Geosci, 94: 131–139

    Google Scholar 

  • 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

    Google Scholar 

  • Herman P A, Prasad G, McGinnity T M. 2017. Designing an interval type-2 fuzzy logic system for handling uncertainty effects in brain-computer interface classification of motor imagery induced EEG patterns. IEEE Trans Fuzzy Syst, 25: 29–42

    Google Scholar 

  • Hwang C, Rhee F C H. 2007. Uncertain fuzzy clustering: Interval type-2 fuzzy approach to C-means. IEEE Trans Fuzzy Syst, 15: 107–120

    Google Scholar 

  • Irpino A, Verde R. 2008. Dynamic clustering of interval data using a Wasserstein-based distance. Pattern Recognit Lett, 29: 1648–1658

    Google Scholar 

  • Javadian M, Bagheri Shouraki S, Sheikhpour Kourabbaslou S. 2017. A novel density-based fuzzy clustering algorithm for low dimensional feature space. Fuzzy Sets Syst, 318: 34–55

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Long T N, Nguyen D D. 2012. Land cover classification using interval type-2 fuzzy clustering for multi-spectral satellite imagery. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC). Seoul 2371–2376

    Google Scholar 

  • Long T N, Mai D S, Pedrycz W. 2015. Semi-supervising interval type-2 fuzzy C-means clustering with spatial information for multi-spectral satellite image classification and change detection. Comput Geosci, 2015, 83: 1–16

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Mendel J M. 2013. On KM algorithms for solving type-2 fuzzy set problems. IEEE Trans Fuzzy Syst, 21: 426–446

    Google Scholar 

  • Mendel J M, John R I, Liu F. 2006. Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst, 14: 808–821

    Google Scholar 

  • 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

    Google Scholar 

  • Moore R E. 1966. Interval Analysis. Englewood Cliffs: Prentice-Hall

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Rocchini D, Foody G M, Nagendra H, Ricotta C, Anand M, He K S, Amici V, Kleinschmit B, Förster M, Schmidtlein S, Feilhauer H, Ghisla A, Metz M, Neteler M. 2013. Uncertainty in ecosystem mapping by remote sensing. Comput Geosci, 50: 128–135

    Google Scholar 

  • Ramos-Guajardo A B, Grzegorzewski P. 2016. Distance-based linear discriminant analysis for interval-valued data. Inf Sci, 372: 591–607

    Google Scholar 

  • Lopes S C, Pumi G, Zaniol K. 2013. Mallows Distance in VARFIMA(0, d, 0) Processes. Commun Stat-Simul Comput, 42: 24–51

    Google Scholar 

  • Tran L, Duckstein L. 2002. Comparison of fuzzy numbers using a fuzzy distance measure. Fuzzy Sets Syst, 130: 331–341

    Google Scholar 

  • 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

    Google Scholar 

  • Tizhoosh H R. 2005. Image thresholding using type II fuzzy sets. Pattern Recognit, 38: 2363–2372

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Xie J, Zhang X. 2012. Clustering of hyper spectral image based on improved fuzzy C means algorithm. J Converg Inf Tech, 7: 320–327

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Zadeh L A. 1975. The concept of a linguistic variable and its application to approximate reasoning—I. Inf Sci, 8: 199–249

    Google Scholar 

  • Zeng J, Liu Z Q. 2007. Type-2 Fuzzy Sets for Pattern Classification: A Review. In: IEEE Symposium on Foundations of Computational Intelligence, 2007. FOCI 2007. Honolulu. 193–200

    Google Scholar 

  • 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

    Google Scholar 

  • Zhong Y, Ma A, Zhang L. 2014. An adaptive memetic fuzzy clustering algorithm with spatial information for remote sensing imagery. IEEE J Sel Top Appl Earth Observations Remote Sens, 7: 1235–1248

    Google Scholar 

  • 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

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Institute of RS and Geographic Information Systems in Guangdong Province, China, for providing the experimental data for this article, YING Shuowen & CHEN Juping from the Surveying & Mapping Institute in Zhuhai City, China, and WANG Lin & LI Fei from the Department of Communications in Qinghai, China, for the reference data support. This work was supported by the National Natural Science Foundation of China (Grant No. 41672323), the Major Scientific Research Project for Universities of Guangdong Province (Grant Nos. 2016KTSCX167, 201612008QX & 2017KTSCX207), the Natural Science Foundation of Guangdong Province, China (Grant Nos. 2016A030313384 & 2016A030313385), and the Hainan Provincial Natural Science Foundation of China (Grant Nos. 20156227 & 618MS058).

Author information

Authors and Affiliations

  1. College of Information Technology, Beijing Normal University, Zhuhai, 519087, China

    Hui He

  2. College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China

    Haihua Xing, Dan Hu & Xianchuan Yu

  3. Guangdong Province Key Laboratory for Land Use and Consolidation, Guangzhou, 510642, China

    Hui He

Authors
  1. Hui He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haihua Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dan Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xianchuan Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Hui He or Xianchuan Yu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, H., Xing, H., Hu, D. et al. Novel fuzzy uncertainty modeling for land cover classification based on clustering analysis. Sci. China Earth Sci. 62, 438–450 (2019). https://doi.org/10.1007/s11430-017-9224-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11430-017-9224-6

Keywords

Search

Navigation