Acta Geodaetica et Cartographica Sinica ›› 2016, Vol. 45 ›› Issue (4): 458-465.doi: 10.11947/j.AGCS.2016.20150123

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A Space-time Interpolation Method of Missing Data Based on Spatio-temporal Heterogeneity

FAN Zide1, GONG Jianya2, LIU Bo3, LI Jialin1, DENG Min1   

  1. 1. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;
    2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
    3. NEC Labs, Beijing 100084, ChinaAbstract
  • Received:2015-03-09 Revised:2016-02-02 Online:2016-04-20 Published:2016-04-28
  • Supported by:
    The National High Technology Research and Development Program of China(863 Program)(No.2013AA122301);The Hunan Funds for Excellent Doctoral Dissertation(No.CX2014B050);The Central South University Funds for Excellent Doctoral Dissertation(No.2015zzts067)

Abstract: Space-time interpolation is widely used to estimate missing data in a dataset integrating both spatial and temporal records. Although space-time interpolation plays a key role in space-time modeling, it is still challenging to model heterogeneity of space-time data in the interpolation model.To overcome this limitation, in this study, a novel space-time interpolation method based on spatio-temporal heterogeneity is proposed to estimate missing data of space-time datasets. Firstly, space partitioning and time slicing of space-time data was implemented. Then the estimates of missing data are computed using space-time surrounding records with heterogeneous spatio-temporal covariance model.Further the weights of space and time are determined using the correlation coefficient and the finally estimates of missing data is combined integrating time and space estimates. Finally, two datasets are selected to verify the accuracy of this method. Experimental results show that the proposed method outperforms the four state-of-the-art methods with higher accuracy and applicability.

Key words: spatio-temporal interpolation, partitioning, heterogeneity, missing data

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