Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes
To address problems in remote sensing image change detection, this study proposes a method for identifying spurious changes based on an eco-geographical zoning knowledge base and crowdsourced data mining. After preliminary change detection using the super pixel cosegmentation method, eco-geographica...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/16/3244 |
| _version_ | 1797522166954065920 |
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| author | Ling Zhu Dejun Gao Tao Jia Jingyi Zhang |
| author_facet | Ling Zhu Dejun Gao Tao Jia Jingyi Zhang |
| author_sort | Ling Zhu |
| collection | DOAJ |
| description | To address problems in remote sensing image change detection, this study proposes a method for identifying spurious changes based on an eco-geographical zoning knowledge base and crowdsourced data mining. After preliminary change detection using the super pixel cosegmentation method, eco-geographical zoning is introduced, and the rules of spurious change are collected based on the knowledge of expert interpreters, and from statistics on existing land cover products according to each eco-geographical zone. Uncertain changed patches with a high possibility of spurious change according to the eco-geographical zoning rule were published in the form of a map service on an online platform, and then crowd tagging information on spurious changed patches was collected. The Hyperlink-Induced Topic Search (HITS) algorithm was used to calculate the spurious change degree of changed patches. We selected the northern part of Laos as the experimental area and the Chinese GF-1 Wide Field View (WFV) images for change detection to verify the effectiveness of the method. The results show that the accuracy of change detection improves by 23% after removing the spurious changes. Spurious changes caused by clouds, river water turbidity, spectral differences in cultivated land before and after harvest, and changes in shrubs, grassland, and forest density, can be removed using an eco-geographical zoning knowledge base and crowdsourced data mining methods. |
| first_indexed | 2024-03-10T08:25:38Z |
| format | Article |
| id | doaj.art-aa29591f88de431bb7126665fc7ef361 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T08:25:38Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-aa29591f88de431bb7126665fc7ef3612023-11-22T09:34:26ZengMDPI AGRemote Sensing2072-42922021-08-011316324410.3390/rs13163244Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover ChangesLing Zhu0Dejun Gao1Tao Jia2Jingyi Zhang3School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaTo address problems in remote sensing image change detection, this study proposes a method for identifying spurious changes based on an eco-geographical zoning knowledge base and crowdsourced data mining. After preliminary change detection using the super pixel cosegmentation method, eco-geographical zoning is introduced, and the rules of spurious change are collected based on the knowledge of expert interpreters, and from statistics on existing land cover products according to each eco-geographical zone. Uncertain changed patches with a high possibility of spurious change according to the eco-geographical zoning rule were published in the form of a map service on an online platform, and then crowd tagging information on spurious changed patches was collected. The Hyperlink-Induced Topic Search (HITS) algorithm was used to calculate the spurious change degree of changed patches. We selected the northern part of Laos as the experimental area and the Chinese GF-1 Wide Field View (WFV) images for change detection to verify the effectiveness of the method. The results show that the accuracy of change detection improves by 23% after removing the spurious changes. Spurious changes caused by clouds, river water turbidity, spectral differences in cultivated land before and after harvest, and changes in shrubs, grassland, and forest density, can be removed using an eco-geographical zoning knowledge base and crowdsourced data mining methods.https://www.mdpi.com/2072-4292/13/16/3244eco-geographical zoningknowledge basecrowdsourcing data miningland coverspurious change |
| spellingShingle | Ling Zhu Dejun Gao Tao Jia Jingyi Zhang Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes Remote Sensing eco-geographical zoning knowledge base crowdsourcing data mining land cover spurious change |
| title | Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes |
| title_full | Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes |
| title_fullStr | Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes |
| title_full_unstemmed | Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes |
| title_short | Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes |
| title_sort | using eco geographical zoning data and crowdsourcing to improve the detection of spurious land cover changes |
| topic | eco-geographical zoning knowledge base crowdsourcing data mining land cover spurious change |
| url | https://www.mdpi.com/2072-4292/13/16/3244 |
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