A Novel Impervious Surface Extraction Method Integrating POI, Vehicle Trajectories, and Satellite Imagery
Impervious surfaces are essential elements for the urban ecological environment. Machine-learning-based approaches have achieved successful breakthroughs in impervious surface extraction. These methods require large sets of labeled impervious surface data to train a model. However, it is a challenge...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9511292/ |
| _version_ | 1818643488173981696 |
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| author | Yiliang Wan Yuwen Fei Tao Wu Rui Jin Tong Xiao |
| author_facet | Yiliang Wan Yuwen Fei Tao Wu Rui Jin Tong Xiao |
| author_sort | Yiliang Wan |
| collection | DOAJ |
| description | Impervious surfaces are essential elements for the urban ecological environment. Machine-learning-based approaches have achieved successful breakthroughs in impervious surface extraction. These methods require large sets of labeled impervious surface data to train a model. However, it is a challenge to acquire massive impervious surface sample data because of complexity, time consumption, and high cost. To address this issue, we explore a method to generate massive impervious surface training samples using point of interest (POI) data and vehicle trajectory global positioning system data. Furthermore, a neural-network-based method is proposed for impervious surface extraction based on the generated training samples. One Landsat-8 image of Shenzhen City, China, was selected to test our approach. The extraction accuracy of the impervious surface was 90.88%, and the overall accuracy based on this method was improved by 8.57% and 8.45% compared with the support vector data description and weighted one-class support vector machine methods, respectively. The results show that the method integrating POI, trajectory data, and satellite imagery can be a viable candidate for impervious surface extraction. |
| first_indexed | 2024-12-16T23:59:45Z |
| format | Article |
| id | doaj.art-7060c7d064394510aa7c64d9d77ef489 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-16T23:59:45Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-7060c7d064394510aa7c64d9d77ef4892022-12-21T22:11:07ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-01148804881410.1109/JSTARS.2021.31037859511292A Novel Impervious Surface Extraction Method Integrating POI, Vehicle Trajectories, and Satellite ImageryYiliang Wan0https://orcid.org/0000-0001-7346-3442Yuwen Fei1Tao Wu2https://orcid.org/0000-0003-3455-7934Rui Jin3Tong Xiao4Key Laboratory of Geospatial Big Data Mining and Application, the School of Geographic Sciences, Hunan Normal University, Changsha, ChinaKey Laboratory of Geospatial Big Data Mining and Application, the School of Geographic Sciences, Hunan Normal University, Changsha, ChinaKey Laboratory of Geospatial Big Data Mining and Application, the School of Geographic Sciences, Hunan Normal University, Changsha, ChinaSchool of Architecture, Hunan University, Changsha, ChinaKey Laboratory of Geospatial Big Data Mining and Application, the School of Geographic Sciences, Hunan Normal University, Changsha, ChinaImpervious surfaces are essential elements for the urban ecological environment. Machine-learning-based approaches have achieved successful breakthroughs in impervious surface extraction. These methods require large sets of labeled impervious surface data to train a model. However, it is a challenge to acquire massive impervious surface sample data because of complexity, time consumption, and high cost. To address this issue, we explore a method to generate massive impervious surface training samples using point of interest (POI) data and vehicle trajectory global positioning system data. Furthermore, a neural-network-based method is proposed for impervious surface extraction based on the generated training samples. One Landsat-8 image of Shenzhen City, China, was selected to test our approach. The extraction accuracy of the impervious surface was 90.88%, and the overall accuracy based on this method was improved by 8.57% and 8.45% compared with the support vector data description and weighted one-class support vector machine methods, respectively. The results show that the method integrating POI, trajectory data, and satellite imagery can be a viable candidate for impervious surface extraction.https://ieeexplore.ieee.org/document/9511292/Impervious surfacepoint of interest (POI)satellite imagesupport vector data description (SVDD)trajectory |
| spellingShingle | Yiliang Wan Yuwen Fei Tao Wu Rui Jin Tong Xiao A Novel Impervious Surface Extraction Method Integrating POI, Vehicle Trajectories, and Satellite Imagery IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Impervious surface point of interest (POI) satellite image support vector data description (SVDD) trajectory |
| title | A Novel Impervious Surface Extraction Method Integrating POI, Vehicle Trajectories, and Satellite Imagery |
| title_full | A Novel Impervious Surface Extraction Method Integrating POI, Vehicle Trajectories, and Satellite Imagery |
| title_fullStr | A Novel Impervious Surface Extraction Method Integrating POI, Vehicle Trajectories, and Satellite Imagery |
| title_full_unstemmed | A Novel Impervious Surface Extraction Method Integrating POI, Vehicle Trajectories, and Satellite Imagery |
| title_short | A Novel Impervious Surface Extraction Method Integrating POI, Vehicle Trajectories, and Satellite Imagery |
| title_sort | novel impervious surface extraction method integrating poi vehicle trajectories and satellite imagery |
| topic | Impervious surface point of interest (POI) satellite image support vector data description (SVDD) trajectory |
| url | https://ieeexplore.ieee.org/document/9511292/ |
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