Journal of Geo-information Science ›› 2017, Vol. 19 ›› Issue (4): 447-456.doi: 10.3724/SP.J.1047.2017.0447

• Orginal Article • Previous Articles     Next Articles

A Raster-Oriented Clustering Method with Space-Attribute Constraints

LIU Jingyi1,2(), XUE Cunjin2,3,*(), FAN Yanguo1, KONG Fanping2, HE Yawen1   

  1. 1. School of Geosciences, China University of Petroleum (East of China), Qingdao 266580, China
    2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
    3. Key Laboratory of Earth Observation, Sanya 572029, China
  • Received:2016-07-12 Revised:2016-11-02 Online:2017-04-20 Published:2017-04-20
  • Contact: XUE Cunjin;


For dealing with the raster datasets, most of the traditional clustering methods are based on the thematic attribute, which separate the integrities of spatial and thematic characteristics. However, the current clustering methods considering both spatial and thematic characteristics still have great problems such as complicated clusters, computational complexities and many input parameters, etc. Thus, this paper presents a Raster-oriented Clustering Method with Space-Attribute Constraints, named RoCMSAC. The core idea of RoCMSAC uses the spatial contiguities and the connectivity of raster datasets to redefine the similarity measure criterion. The RoCMSAC consists of three steps, i.e. the cluster generation with the homogeneous attributes, the cluster merging with the spatial contiguities and the cluster merging with the spatial vicinities. Finally, the feasibility and effectiveness of the algorithm are validated with the datasets of sea surface temperature in Pacific Ocean. The clusters from RoCMSAC are compared with those from K-Mean and DDBSC. The results show that: (1) RoCMSAC can detect any grid cluster with the complicated shape, which needs less time and fewer input parameters; (2) The clusters from RoCMSAC obtain both the proximity in spatial domain and the homogeneity in attribute one.

Key words: raster data, data mining, spatial clustering, dual constraints, Pacific Ocean