Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93593
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
dc.creator | Wu, W | en_US |
dc.creator | Miao, Z | en_US |
dc.creator | Xiao, Y | en_US |
dc.creator | Li, Z | en_US |
dc.creator | Zhang, A | en_US |
dc.creator | Samat, A | en_US |
dc.creator | Du, N | en_US |
dc.creator | Xu, Z | en_US |
dc.creator | Gamba, P | en_US |
dc.date.accessioned | 2022-07-14T05:15:11Z | - |
dc.date.available | 2022-07-14T05:15:11Z | - |
dc.identifier.issn | 1939-1404 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93593 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information see https://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
dc.rights | The following publication Wu, W., Miao, Z., Xiao, Y., Li, Z., Zhang, A., Samat, A., ... & Gamba, P. (2020). New Scheme for Impervious Surface Area Mapping From SAR Images With Auxiliary User-Generated Content. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5954-5970 is available at https://doi.org/10.1109/JSTARS.2020.3027507 | en_US |
dc.subject | Clustering-based one-class support vector machine | en_US |
dc.subject | Impervious surface area (ISA) | en_US |
dc.subject | User-generated content (UGC) | en_US |
dc.subject | Synthetic-aperture radar (SAR) | en_US |
dc.title | New scheme for impervious surface area mapping from SAR images with auxiliary user-generated content | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 5954 | en_US |
dc.identifier.epage | 5970 | en_US |
dc.identifier.volume | 13 | en_US |
dc.identifier.doi | 10.1109/JSTARS.2020.3027507 | en_US |
dcterms.abstract | This article presents a new scheme to extract impervious surface area from synthetic-aperture radar (SAR) images exploiting auxiliary user-generated content (UGC). The presented scheme includes the automatic generation of training samples based on the combination of UGC and SAR data, and SAR data preprocessing, leading to impervious surface area classification through a clustering-based one-class support vector machine approach. Two areas-namely, the cities of Beijing and Taipei, have been analyzed using the Sentinel-1 SAR data to test and validate the proposed methodology. Experimental results show that the presented scheme improves the automatic selection of impervious surface training samples. Moreover, this scheme achieves a comparable classification performance to traditional methods without requiring time-consuming training point manual extraction. Results in this study will help to promote the application of UGC for urban remote sensing data interpretation. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE journal of selected topics in applied earth observations and remote sensing, 2020, v. 13, p. 5954-5970 | en_US |
dcterms.isPartOf | IEEE journal of selected topics in applied earth observations and remote sensing | en_US |
dcterms.issued | 2020 | - |
dc.identifier.isi | WOS:000577878900003 | - |
dc.identifier.scopus | 2-s2.0-85093954490 | - |
dc.identifier.eissn | 2151-1535 | en_US |
dc.description.validate | 202207 bcvc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | LSGI-0480 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences under Grant G2019-02-06; National Natural Science Foundation of China under Grant 41701500; Natural Science Foundation of Hunan Province under Grant 2018JJ3641 and Grant 2019JJ60001; Scientific Research Fund of Hunan Provincial Education Department under Grant 13B129; Natural Science Foundation of Jiangsu Province under Grant BK20190785; Innovation-Driven Project of Central South University under Grant 2020CX036; Natural Science Foundation of Jiangsu Province under Grant BK20190785, and Postgraduate Innovation Project of Central South University under Grant 2020ZZTS693. | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 43053908 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Wu_New_Scheme_Impervious.pdf | 18.87 MB | Adobe PDF | View/Open |
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