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Extraction of landslide features in UAV remote sensing images based on machine vision and image enhancement technology

  • S.I.: Machine Learning based semantic representation and analytics for multimedia application
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Abstract

To improve the effect of landslide feature extraction, this paper improves the remote sensing image recognition algorithm with the support of a machine learning algorithm. Moreover, this paper combines UAV remote sensing images to extract landslide features, classifies and introduces the evaluation criteria for target detection and several representative target detectors. This paper also constructs the functional structure of the system according to the landslide feature extraction requirements and designs a set of optimization schemes for landslide feature data collection and control measurement suitable for field operations. In addition, this paper analyses the system kernel algorithm process and analyses the system function realization through simulation research. Finally, this paper designs an experiment to evaluate the practicability of the system constructed in this paper. From the results of experimental statistics, we can see that the system constructed in this paper has good practicability.

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Authors and Affiliations

  1. Blockchain Laboratory of Agricultural Vegetables, Weifang University of Science and Technology, Weifang, 262700, China

    Jianhua Qi

  2. College of Information and Computer Engineering, Pingxiang University, Pingxiang, 337055, China

    Hong Chen

  3. Department of Mechanical and Electrical Information, Weifang University of Science and Technology, Weifang, 262700, China

    Fengping Chen

Authors
  1. Jianhua Qi
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  2. Hong Chen
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  3. Fengping Chen
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Correspondence to Hong Chen.

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Qi, J., Chen, H. & Chen, F. Extraction of landslide features in UAV remote sensing images based on machine vision and image enhancement technology. Neural Comput & Applic 34, 12283–12297 (2022). https://doi.org/10.1007/s00521-021-06523-4

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  • DOI: https://doi.org/10.1007/s00521-021-06523-4

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