Science China Earth Sciences

, Volume 62, Issue 2, pp 438–450 | Cite as

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

  • Hui HeEmail author
  • Haihua Xing
  • Dan Hu
  • Xianchuan YuEmail author
Research Paper


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.


Interval-valued data Type-2 fuzzy sets Type reduction Type-2 fuzzy clustering Land cover classification 


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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).


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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information TechnologyBeijing Normal UniversityZhuhaiChina
  2. 2.College of Information Science and TechnologyBeijing Normal UniversityBeijingChina
  3. 3.Guangdong Province Key Laboratory for Land Use and ConsolidationGuangzhouChina

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