Paper
12 May 2022 Research on extraction method of external force damage based on multi-temporal remote sensing image
Guanghua He, Xueling Huang, Zhijian Zhang, Wei Zhang, Jun Xu, Ke Sun, Chenyu Tian
Author Affiliations +
Proceedings Volume 12173, International Conference on Optics and Machine Vision (ICOMV 2022); 121730N (2022) https://doi.org/10.1117/12.2634644
Event: International Conference on Optics and Machine Vision (ICOMV 2022), 2022, Guangzhou, China
Abstract
Remote sensing can be used to monitor and warn of the damage of external force caused by human activities such as road construction and building construction along the underground cable. The extraction of external force damage information is mainly based on the results of change detection. Based on the object-oriented change detection method, our study selects the optimal features through multi-scale segmentation, multi-source feature construction and classifies the two-time high-resolution satellite remote sensing images. Using the classification results to establish the hierarchical relationship, the idea of superposition analysis is introduced, and the detection results of ground object changes are obtained by logical difference operation, then the external rupture information is extracted. This method avoids the subjectivity and complexity of the varying threshold method. Our results show that the detection probability is 88.63%, the false detection probability is 11.36%, and the miss detection probability is 4.41%.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Guanghua He, Xueling Huang, Zhijian Zhang, Wei Zhang, Jun Xu, Ke Sun, and Chenyu Tian "Research on extraction method of external force damage based on multi-temporal remote sensing image", Proc. SPIE 12173, International Conference on Optics and Machine Vision (ICOMV 2022), 121730N (12 May 2022); https://doi.org/10.1117/12.2634644
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KEYWORDS
Image segmentation

Remote sensing

Satellites

Analytical research

Image classification

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