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Geomatics and Information Science of Wuhan University. 2014, (08) Core

The Geographic Object-based Method for Change Detection with Remote Sensing ImageryChinese Full Text

DIAN Yuanyong;FANG Shenghui;YAO Chonghuai;College of Horticulture and Forestry,Huazhong Agricultural University;School of Remote Sensing and Information Engineering,Wuhan University;

Abstract: This paper proposes a geographical object-based method for change detection with high resolution images based on the changing areas distributed as a clustered type.This algorithm utilizes the Mean-Shift segmentation algorithm to extract a geographic object,and then uses the gray information of the geographic object with the EM algorithm to automatically extract changed and unchanged areas.This method considers spatial neighborhood information which can avoid the isolation and discrete disconnected areas in change results when using apixel-based method.This method also reduces intervention when determining the change threshold value.Groups of three different spatial resolution images（QuickBird,SPOT,TM images）are used to verify this proposed geographic object-based change detection algorithm and compared the accuracy and precision with a pixel-base method.Our results show that the accuracy with object-based change detection method on QuickBird,SPOT and TM images was 91.1%,87.3% and 84.3%,while for the pixel-based method are 86.41%,82.48% and81.02%respectively.These results illustrate that the object-based change detection method has higher change detection accuracy than the pixel based approach.Moreover,the object-based method has better accuracy for high spatial resolution than in middle or low resolution images.
Keywords：

change detection; image segment; geographic object-based; Mean-Shift; expectation Maximization（EM）;

  • DOI：

    10.13203/j.whugis20130053

  • Series：

  • Subject：

  • Classification Code：

    P237

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