Jing Ren
*
Jiakun Wang
*The final, formatted version of the article will be published soon.
ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Geohazards and Georisks
Volume 12 - 2024 |
doi: 10.3389/feart.2024.1473904
This article is part of the Research Topic Monitoring, Early Warning and Mitigation of Natural and Engineered Slopes – Volume IV View all 15 articles
Remote sensing identification of shallow landslide based on improved Otsu algorithm and multi feature threshold
Provisionally accepted- Sichuan Provincial Seventh Geological Brigade, Leshan, China
In low-resolution remote sensing images under complex lighting conditions, there is a similarity in spectral characteristics between non-landslide areas and landslide bodies, which increases the probability of misjudgment in the identification process of shallow landslide bodies. In order to further improve the accuracy of landslide identification, a shallow landslide remote sensing identification method based on an improved Otsu algorithm and multi-feature threshold is proposed for the temporary treatment project of the Yangjunba disaster site in Leshan City. Using Retinex theory, remote sensing images are enhanced with local linear models and guided filtering; then, multi-feature scales and sliding window calculations of opening and closing transformations identify potential landslide areas, which are finally segmented using the Otsu algorithm. Through experimental verification, the method proposed in this article can clearly segment the target object and background after binary segmentation of remote sensing images. The recognition rate of shallow landslide bodies is not less than 95%, indicating that the method proposed in this article is relatively accurate in identifying shallow landslide bodies in the research area and has good application effects. 删除[Filip Gurkalo]: 删除[Filip Gurkalo]: 删除[Filip Gurkalo]: filtering; then 删除[Filip Gurkalo]: , multi 删除[Filip Gurkalo]: which not only improves 删除[Filip Gurkalo]: , but also 删除[Filip Gurkalo]: es 删除[Filip Gurkalo]: complex backgrounds and distinguish various spectral features between non-landslide areas and landslide bodies, this paper uses multiple feature thresholds to remove invalid or redundant background features, thereby improving the accuracy of image segmentation. The traditional Otsu algorithm can perform image segmentation by automatically selecting a threshold in image In reference(Jiang et al., 2023), 删除[
Keywords: Otsu algorithm1, Multi feature threshold2, Remote sensing images3, Landslide mass4, image segmentation5
Received: 31 Jul 2024; Accepted: 27 Aug 2024.
Copyright: © 2024 Ren, Wang, Chen, Li, Xu, Yan and Song. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jing Ren, Sichuan Provincial Seventh Geological Brigade, Leshan, China
Jiakun Wang, Sichuan Provincial Seventh Geological Brigade, Leshan, China
Rui Chen, Sichuan Provincial Seventh Geological Brigade, Leshan, China
Hong Li, Sichuan Provincial Seventh Geological Brigade, Leshan, China
Dongli Xu, Sichuan Provincial Seventh Geological Brigade, Leshan, China
Lihua Yan, Sichuan Provincial Seventh Geological Brigade, Leshan, China
Jingyuan Song, Sichuan Provincial Seventh Geological Brigade, Leshan, China
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