A study of high-resolution remote sensing image landslide detection with optimized anchor boxes and edge enhancement
This paper takes landslide as a special research object. For the problems of landslide detection in remote sensing images, deep learning and playback method is adopted. Using the You Only Look Once v5 network (YOLOv5) in combination with the Gabor filter, its edge detection, detection anchor frame a...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | European Journal of Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2023.2289616 |
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| author | Kun Wang Ling Han Juan Liao |
| author_facet | Kun Wang Ling Han Juan Liao |
| author_sort | Kun Wang |
| collection | DOAJ |
| description | This paper takes landslide as a special research object. For the problems of landslide detection in remote sensing images, deep learning and playback method is adopted. Using the You Only Look Once v5 network (YOLOv5) in combination with the Gabor filter, its edge detection, detection anchor frame and small object detection scale are improved and optimized. The YOLOv5(ISODATA) model was finally established for landslide image detection by incorporating the edge control factor and four clustering algorithms (K-means, K-means + +, k-medoid, and Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) to evaluate the accuracy of the detection anchor frame and add small target large-scale sampling. Three target identification models – YOLOv5, Region Convolution Neural Network (R-CNN), and Fast R-CNN – are experimentally compared in order to assess the effectiveness of the proposed method. According to the results of experiments, the proposed method outperforms the other three detection models with an AUC of 0.921, a recall of 86.14%, and an MCC of 0.887. It further demonstrates the method’s positive impact on landslide remote sensing image recognition and its ability to solve related issues. |
| format | Article |
| id | doaj-art-9d85c9dbb7a84aa2b4406fd73db6d470 |
| institution | OA Journals |
| issn | 2279-7254 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj-art-9d85c9dbb7a84aa2b4406fd73db6d4702025-08-20T01:58:55ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542024-12-0157110.1080/22797254.2023.2289616A study of high-resolution remote sensing image landslide detection with optimized anchor boxes and edge enhancementKun Wang0Ling Han1Juan Liao2College of Earth Science and Resources, Chang’an University, Xi’an, ChinaCollege of Land Engineering, Chang’an University, Xi’an, ChinaCollege of Geography and Tourism, Hengyang Normal University, Heng Yang, ChinaThis paper takes landslide as a special research object. For the problems of landslide detection in remote sensing images, deep learning and playback method is adopted. Using the You Only Look Once v5 network (YOLOv5) in combination with the Gabor filter, its edge detection, detection anchor frame and small object detection scale are improved and optimized. The YOLOv5(ISODATA) model was finally established for landslide image detection by incorporating the edge control factor and four clustering algorithms (K-means, K-means + +, k-medoid, and Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) to evaluate the accuracy of the detection anchor frame and add small target large-scale sampling. Three target identification models – YOLOv5, Region Convolution Neural Network (R-CNN), and Fast R-CNN – are experimentally compared in order to assess the effectiveness of the proposed method. According to the results of experiments, the proposed method outperforms the other three detection models with an AUC of 0.921, a recall of 86.14%, and an MCC of 0.887. It further demonstrates the method’s positive impact on landslide remote sensing image recognition and its ability to solve related issues.https://www.tandfonline.com/doi/10.1080/22797254.2023.2289616Landslide1edge detection2detection anchor frame3YOLOv5 (ISODATA) model4remote sensing intelligence5 |
| spellingShingle | Kun Wang Ling Han Juan Liao A study of high-resolution remote sensing image landslide detection with optimized anchor boxes and edge enhancement European Journal of Remote Sensing Landslide1 edge detection2 detection anchor frame3 YOLOv5 (ISODATA) model4 remote sensing intelligence5 |
| title | A study of high-resolution remote sensing image landslide detection with optimized anchor boxes and edge enhancement |
| title_full | A study of high-resolution remote sensing image landslide detection with optimized anchor boxes and edge enhancement |
| title_fullStr | A study of high-resolution remote sensing image landslide detection with optimized anchor boxes and edge enhancement |
| title_full_unstemmed | A study of high-resolution remote sensing image landslide detection with optimized anchor boxes and edge enhancement |
| title_short | A study of high-resolution remote sensing image landslide detection with optimized anchor boxes and edge enhancement |
| title_sort | study of high resolution remote sensing image landslide detection with optimized anchor boxes and edge enhancement |
| topic | Landslide1 edge detection2 detection anchor frame3 YOLOv5 (ISODATA) model4 remote sensing intelligence5 |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2023.2289616 |
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