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Extraction of Change Information based on Multi-sequence Image Objects

Zhang, Haitao; Cheng, Xinwen

European journal of remote sensing. Volume 49:Issue 1 (2016); pp 465-487 -- Associazione italiana di telerilevamento -- Taylor & Francis

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  • Title:
    Extraction of Change Information based on Multi-sequence Image Objects
  • Author: Zhang, Haitao;
    Cheng, Xinwen
  • Found In: European journal of remote sensing. Volume 49:Issue 1 (2016); pp 465-487
  • Journal Title: European journal of remote sensing
  • Subjects: Remote sensing--Periodicals; Periodicals; Remote sensing; Change detection--arithmetic progression--segmentation scale--sub-objects--feature vector; Electronic journals; Dewey: 621.3678
  • Rights: legaldeposit
  • Publication Details: Associazione italiana di telerilevamento
    Taylor & Francis
  • Abstract: Abstract:

    For identification of change information, most studies focus on pixel-based techniques for low-resolution images, while few studies have examined object-based techniques for high-resolution images. Moreover, most of the techniques are complex and have a high requirement for the segmentation scale. This paper proposes a change detection method based on multi-sequence image objects and introduces the use of arithmetic progression to generate the set of segmentation scales. Pre-event and post-event images are segmented with multi-scales, respectively, and sub-objects are obtained based on the division of the minimum segmentation scales of bi-temporal images. Change feature vectors are constructed for each associated object of sub-object and vectors' magnitude is computed. After the determination of change threshold values, the change feature vectors are used to confirm whether sub-objects have changed, providing final change information. This method was tested using the bi-temporal World View 2 images taken before and after a landslide. The results confirm the feasibility of the method presented in this paper, and show its high accuracy through a comparison with the changing vector analysis method and the post-classification comparison method based on object-oriented theory. The approach outlined herein would be helpful for extraction of change information in high-resolution images.


  • Identifier: System Number: LDEAvdc_100067557510.0x000001; Journal ISSN: 2279-7254; 10.5721/EuJRS20164925
  • Publication Date: 2016
  • Physical Description: Electronic
  • Shelfmark(s): ELD Digital store

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