Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93519
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
dc.creator | Shi, W | en_US |
dc.creator | Zhang, M | en_US |
dc.creator | Ke, H | en_US |
dc.creator | Fang, X | en_US |
dc.creator | Zhan, Z | en_US |
dc.creator | Chen, S | en_US |
dc.date.accessioned | 2022-07-08T01:02:54Z | - |
dc.date.available | 2022-07-08T01:02:54Z | - |
dc.identifier.issn | 0196-2892 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93519 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Shi, W., Zhang, M., Ke, H., Fang, X., Zhan, Z., & Chen, S. (2020). Landslide recognition by deep convolutional neural network and change detection. IEEE Transactions on Geoscience and Remote Sensing, 59(6), 4654-4672 is available at https://doi.org/10.1109/TGRS.2020.3015826 | en_US |
dc.subject | Change detection | en_US |
dc.subject | Convolutional neural network (CNN) | en_US |
dc.subject | Landslide | en_US |
dc.subject | Remotely sensed (RS) images | en_US |
dc.title | Landslide recognition by deep convolutional neural network and change detection | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 4654 | en_US |
dc.identifier.epage | 4672 | en_US |
dc.identifier.volume | 59 | en_US |
dc.identifier.issue | 6 | en_US |
dc.identifier.doi | 10.1109/TGRS.2020.3015826 | en_US |
dcterms.abstract | It is a technological challenge to recognize landslides from remotely sensed (RS) images automatically and at high speeds, which is fundamentally important for preventing and controlling natural landslide hazards. Many methods have been developed, but there remains room for improvement for stable, higher accuracy, and high-speed landslide recognition for large areas with complex land cover. In this article, a novel integrated approach combining a deep convolutional neural network (CNN) and change detection is proposed for landslide recognition from RS images. Logically, it comprises the following four parts. First, a CNN for landslide recognition is built based on training data sets from RS images with historical landslides. Second, the object-oriented change detection CNN (CDCNN) with a fully connected conditional random field (CRF) is implemented based on the trained CNN. Third, the preliminary CDCNN is optimized by the proposed postprocessing methods. Finally, the results are further enhanced by a set of information extraction methods, including trail extraction, source point extraction, and attribute extraction. Furthermore, in the implementation of the proposed approach, image block processing and parallel processing strategies are adopted. As a result, the speed has been improved significantly, which is extremely important for RS images covering large areas. The effectiveness of the proposed approach has been examined using two landslide-prone sites, Lantau Island and Sharp Peak, Hong Kong, with a total area of more than 70 km2. Besides its high speed, the proposed approach has an accuracy exceeding 80%, and the experiments demonstrate its high practicability. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on geoscience and remote sensing, June 2021, v. 59, no. 6, p. 4654-4672 | en_US |
dcterms.isPartOf | IEEE transactions on geoscience and remote sensing | en_US |
dcterms.issued | 2021-06 | - |
dc.identifier.scopus | 2-s2.0-85094876139 | - |
dc.identifier.eissn | 1558-0644 | en_US |
dc.description.validate | 202207 bcfc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | LSGI-0028 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Hong Kong Polytechnic University Projects through the Landslip Prevention and Mitigation Programme; the Ministry of Science and Technology of the People’s Republic of China | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 52208960 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Shi_Landslide_Recognition_Deep.pdf | Pre-Published version | 11.51 MB | Adobe PDF | View/Open |
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