Acta Geodaetica et Cartographica Sinica ›› 2016, Vol. 45 ›› Issue (3): 362-371.doi: 10.11947/j.AGCS.2016.20150099

Previous Articles     Next Articles

A Quantitative Calculation Method of Composite Spatial Direction Similarity Concerning Scale Differences

CHEN Zhanlong1,2, GONG Xi1, WU Liang1, AN Xiaoya2,3   

  1. 1. Department of Information Engineering, China University of Geosciences, Wuhan 430074, China;
    2. State Key Laboratory of Geography Information Engineering, Xi'an 710054, China;
    3. Xi'an Research Institute of Surveying and Mapping, Xi'an 710054, China
  • Received:2015-02-16 Revised:2015-12-15 Online:2016-03-20 Published:2016-03-25
  • Supported by:
    The National Natural Science Foundation of China(Nos.41401443;41201469);The National Key Technology Research and Development Program of the Ministry of Science and Technology of China(No.2011BAH06B04);Open Research Fund of State Key Laboratory of Geography Information Engineering(No.SKLGIE2013-Z-4-1);Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing(No.13I02);Research Funds for the Central Universities Basic Special Projects(No.CUGL130260)

Abstract: This article introduces a new model for direction relations between multiple spatial objects at multiple scales and a corresponding similarity assessment method. The model is an improvement of direction relation matrix, which quantitatively models direction relations on object scale, and by the idea of decomposition and means of the optimum solution of the transportation problem to solve the minimum conversion cost between multiple direction matrices, namely distance between a pair of matrices, thus quantified the difference between a pair of directions, finally obtain the similarity values between arbitrary pairs of multiple spatial objects and compare the results. Experiments on calculating similarity between objects at different scales show that the presented method is efficient, accurate, and capable of obtaining results consistent with human cognition.

Key words: multi-scales, multiple spatial objects, direction similarity, direction relation matrix, decomposition

CLC Number: