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Information Extraction of High-Resolution Remotely Sensed Image Based on Multiresolution Segmentation

Author

Listed:
  • Peng Shao

    (College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China)

  • Guodong Yang

    (College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China)

  • Xuefeng Niu

    (College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China)

  • Xuqing Zhang

    (College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China)

  • Fulei Zhan

    (College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China)

  • Tianqi Tang

    (College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China)

Abstract

The principle of multiresolution segmentation was represented in detail in this study, and the canny algorithm was applied for edge-detection of a remotely sensed image based on this principle. The target image was divided into regions based on object-oriented multiresolution segmentation and edge-detection. Furthermore, object hierarchy was created, and a series of features (water bodies, vegetation, roads, residential areas, bare land and other information) were extracted by the spectral and geometrical features. The results indicate that the edge-detection has a positive effect on multiresolution segmentation, and overall accuracy of information extraction reaches to 94.6% by the confusion matrix.

Suggested Citation

  • Peng Shao & Guodong Yang & Xuefeng Niu & Xuqing Zhang & Fulei Zhan & Tianqi Tang, 2014. "Information Extraction of High-Resolution Remotely Sensed Image Based on Multiresolution Segmentation," Sustainability, MDPI, vol. 6(8), pages 1-11, August.
    • Handle: RePEc:gam:jsusta:v:6:y:2014:i:8:p:5300-5310:d:39207
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    Cited by:

    1. Chae-Yeon Kim & Jong-Gwan Jeong & So-Won Choi & Eul-Bum Lee, 2022. "An AI-Based Automatic Risks Detection Solution for Plant Owner’s Technical Requirements in Equipment Purchase Order," Sustainability, MDPI, vol. 14(16), pages 1-27, August.

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