A Mountain Summit Recognition Method Based on Improved Faster R-CNN
Mountain summits are vital topographic feature points, which are essential for understanding landform processes and their impacts on the environment and ecosystem. Traditional summit detection methods operate on handcrafted features extracted from digital elevation model (DEM) data and apply paramet...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/8235108 |
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| author | Yueping Kong Yun Wang Song Guo Jiajing Wang |
| author_facet | Yueping Kong Yun Wang Song Guo Jiajing Wang |
| author_sort | Yueping Kong |
| collection | DOAJ |
| description | Mountain summits are vital topographic feature points, which are essential for understanding landform processes and their impacts on the environment and ecosystem. Traditional summit detection methods operate on handcrafted features extracted from digital elevation model (DEM) data and apply parametric detection algorithms to locate mountain summits. However, these methods may no longer be effective to achieve desirable recognition results in small summits and suffer from the objective criterion lacking problem. Thus, to address these problems, we propose an improved Faster region-convolutional neural network (R-CNN) to accurately detect the mountain summits from DEM data. Based on Faster R-CNN, the improved network adopts a residual convolution block to replace the traditional part and adds a feature pyramid network (FPN) to fuse the features with adjacent layers to better address the mountain summit detection task. The residual convolution is employed to capture the deep correlation between visual and physical morphological features. The FPN is utilized to integrate the location and semantic information in the extracted feature maps to effectively represent the mountain summit area. The experimental results demonstrate that the proposed network could achieve the highest recall and precision without manually designed summit features and accurately identify small summits. |
| format | Article |
| id | doaj-art-0a7b5a9a0cef40d68152169b57ea2e56 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0a7b5a9a0cef40d68152169b57ea2e562025-08-20T02:03:08ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/82351088235108A Mountain Summit Recognition Method Based on Improved Faster R-CNNYueping Kong0Yun Wang1Song Guo2Jiajing Wang3School of Information and Control, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaSchool of Information and Control, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaSchool of Information and Control, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaSchool of Information and Control, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaMountain summits are vital topographic feature points, which are essential for understanding landform processes and their impacts on the environment and ecosystem. Traditional summit detection methods operate on handcrafted features extracted from digital elevation model (DEM) data and apply parametric detection algorithms to locate mountain summits. However, these methods may no longer be effective to achieve desirable recognition results in small summits and suffer from the objective criterion lacking problem. Thus, to address these problems, we propose an improved Faster region-convolutional neural network (R-CNN) to accurately detect the mountain summits from DEM data. Based on Faster R-CNN, the improved network adopts a residual convolution block to replace the traditional part and adds a feature pyramid network (FPN) to fuse the features with adjacent layers to better address the mountain summit detection task. The residual convolution is employed to capture the deep correlation between visual and physical morphological features. The FPN is utilized to integrate the location and semantic information in the extracted feature maps to effectively represent the mountain summit area. The experimental results demonstrate that the proposed network could achieve the highest recall and precision without manually designed summit features and accurately identify small summits.http://dx.doi.org/10.1155/2021/8235108 |
| spellingShingle | Yueping Kong Yun Wang Song Guo Jiajing Wang A Mountain Summit Recognition Method Based on Improved Faster R-CNN Complexity |
| title | A Mountain Summit Recognition Method Based on Improved Faster R-CNN |
| title_full | A Mountain Summit Recognition Method Based on Improved Faster R-CNN |
| title_fullStr | A Mountain Summit Recognition Method Based on Improved Faster R-CNN |
| title_full_unstemmed | A Mountain Summit Recognition Method Based on Improved Faster R-CNN |
| title_short | A Mountain Summit Recognition Method Based on Improved Faster R-CNN |
| title_sort | mountain summit recognition method based on improved faster r cnn |
| url | http://dx.doi.org/10.1155/2021/8235108 |
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