Semantic Segmentation Model for Wide-Area Coseismic Landslide Extraction Based on Embedded Multichannel Spectral–Topographic Feature Fusion: A Case Study of the Jiuzhaigou Ms7.0 Earthquake in Sichuan, China
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| Názov: | Semantic Segmentation Model for Wide-Area Coseismic Landslide Extraction Based on Embedded Multichannel Spectral–Topographic Feature Fusion: A Case Study of the Jiuzhaigou Ms7.0 Earthquake in Sichuan, China |
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| Autori: | Xiangxiang Zheng, Lingyi Han, Guojin He, Ning Wang, Guizhou Wang, Lei Feng |
| Zdroj: | Remote Sensing, Vol 15, Iss 4, p 1084 (2023) |
| Informácie o vydavateľovi: | MDPI AG |
| Rok vydania: | 2023 |
| Zbierka: | Directory of Open Access Journals: DOAJ Articles |
| Predmety: | coseismic landslide, feature fusion, remote sensing, DEM, deep learning, DeepLab V3+, Science |
| Popis: | The rapid and accurate extraction of wide-area coseismic landslide locations is critical in earthquake emergencies. At present, the extraction of coseismic landslides is mainly based on post-earthquake site investigation or the interpretation of human–computer interactions based on remote sensing images. However, the identification efficiency is low, which seriously delays the earthquake emergency response. On the basis of the available multisource and multiscale remote sensing data, numerous studies have been carried out on the methods of coseismic landslide extraction, such as pixel analysis, object-oriented analysis, change detection, and machine learning. However, the effectiveness of coseismic landslide extraction was low in wide areas with complex topographic and geomorphic backgrounds. Therefore, this paper offers a comprehensive study of the factors influencing coseismic landslides and researches rapid and accurate wide-area coseismic landslide extraction methods with multisource remote sensing and geoscience technology. These techniques include digital elevation modeling (DEM) and its derived slopes and aspects. An embedded multichannel spectral–topographic feature fusion model for coseismic landslide extraction based on DeepLab V3+ is proposed, and a knowledge-enhanced deep learning information extraction method integrating geological knowledge is formed. Using the Jiuzhaigou Ms7.0 earthquake (seismic intensity VIII) in Sichuan Province, China, a comparison of landslide extraction models and strategies is carried out. The results show that the model proposed in this paper achieves the best balance in the accuracy and efficiency of wide-area extractions. Using multiple feature data of coseismic landslides, the problem of mixed pixels is solved. The rate of the misidentification of landslides as clouds, snow, buildings, and roads is significantly lower than in other methods. The identified landslide boundaries are smoother and more accurate, and the connectivity is better. Compared with other methods, ... |
| Druh dokumentu: | article in journal/newspaper |
| Jazyk: | English |
| Relation: | https://www.mdpi.com/2072-4292/15/4/1084; https://doaj.org/toc/2072-4292; https://doaj.org/article/7dfecef12dd14e459f6dd2937e4b4a78 |
| DOI: | 10.3390/rs15041084 |
| Dostupnosť: | https://doi.org/10.3390/rs15041084 https://doaj.org/article/7dfecef12dd14e459f6dd2937e4b4a78 |
| Prístupové číslo: | edsbas.5DA194E6 |
| Databáza: | BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: Semantic Segmentation Model for Wide-Area Coseismic Landslide Extraction Based on Embedded Multichannel Spectral–Topographic Feature Fusion: A Case Study of the Jiuzhaigou Ms7.0 Earthquake in Sichuan, China – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xiangxiang+Zheng%22">Xiangxiang Zheng</searchLink><br /><searchLink fieldCode="AR" term="%22Lingyi+Han%22">Lingyi Han</searchLink><br /><searchLink fieldCode="AR" term="%22Guojin+He%22">Guojin He</searchLink><br /><searchLink fieldCode="AR" term="%22Ning+Wang%22">Ning Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Guizhou+Wang%22">Guizhou Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Lei+Feng%22">Lei Feng</searchLink> – Name: TitleSource Label: Source Group: Src Data: Remote Sensing, Vol 15, Iss 4, p 1084 (2023) – Name: Publisher Label: Publisher Information Group: PubInfo Data: MDPI AG – Name: DatePubCY Label: Publication Year Group: Date Data: 2023 – Name: Subset Label: Collection Group: HoldingsInfo Data: Directory of Open Access Journals: DOAJ Articles – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22coseismic+landslide%22">coseismic landslide</searchLink><br /><searchLink fieldCode="DE" term="%22feature+fusion%22">feature fusion</searchLink><br /><searchLink fieldCode="DE" term="%22remote+sensing%22">remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22DEM%22">DEM</searchLink><br /><searchLink fieldCode="DE" term="%22deep+learning%22">deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22DeepLab+V3%2B%22">DeepLab V3+</searchLink><br /><searchLink fieldCode="DE" term="%22Science%22">Science</searchLink> – Name: Abstract Label: Description Group: Ab Data: The rapid and accurate extraction of wide-area coseismic landslide locations is critical in earthquake emergencies. At present, the extraction of coseismic landslides is mainly based on post-earthquake site investigation or the interpretation of human–computer interactions based on remote sensing images. However, the identification efficiency is low, which seriously delays the earthquake emergency response. On the basis of the available multisource and multiscale remote sensing data, numerous studies have been carried out on the methods of coseismic landslide extraction, such as pixel analysis, object-oriented analysis, change detection, and machine learning. However, the effectiveness of coseismic landslide extraction was low in wide areas with complex topographic and geomorphic backgrounds. Therefore, this paper offers a comprehensive study of the factors influencing coseismic landslides and researches rapid and accurate wide-area coseismic landslide extraction methods with multisource remote sensing and geoscience technology. These techniques include digital elevation modeling (DEM) and its derived slopes and aspects. An embedded multichannel spectral–topographic feature fusion model for coseismic landslide extraction based on DeepLab V3+ is proposed, and a knowledge-enhanced deep learning information extraction method integrating geological knowledge is formed. Using the Jiuzhaigou Ms7.0 earthquake (seismic intensity VIII) in Sichuan Province, China, a comparison of landslide extraction models and strategies is carried out. The results show that the model proposed in this paper achieves the best balance in the accuracy and efficiency of wide-area extractions. Using multiple feature data of coseismic landslides, the problem of mixed pixels is solved. The rate of the misidentification of landslides as clouds, snow, buildings, and roads is significantly lower than in other methods. The identified landslide boundaries are smoother and more accurate, and the connectivity is better. Compared with other methods, ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: article in journal/newspaper – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://www.mdpi.com/2072-4292/15/4/1084; https://doaj.org/toc/2072-4292; https://doaj.org/article/7dfecef12dd14e459f6dd2937e4b4a78 – Name: DOI Label: DOI Group: ID Data: 10.3390/rs15041084 – Name: URL Label: Availability Group: URL Data: https://doi.org/10.3390/rs15041084<br />https://doaj.org/article/7dfecef12dd14e459f6dd2937e4b4a78 – Name: AN Label: Accession Number Group: ID Data: edsbas.5DA194E6 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs15041084 Languages: – Text: English Subjects: – SubjectFull: coseismic landslide Type: general – SubjectFull: feature fusion Type: general – SubjectFull: remote sensing Type: general – SubjectFull: DEM Type: general – SubjectFull: deep learning Type: general – SubjectFull: DeepLab V3+ Type: general – SubjectFull: Science Type: general Titles: – TitleFull: Semantic Segmentation Model for Wide-Area Coseismic Landslide Extraction Based on Embedded Multichannel Spectral–Topographic Feature Fusion: A Case Study of the Jiuzhaigou Ms7.0 Earthquake in Sichuan, China Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xiangxiang Zheng – PersonEntity: Name: NameFull: Lingyi Han – PersonEntity: Name: NameFull: Guojin He – PersonEntity: Name: NameFull: Ning Wang – PersonEntity: Name: NameFull: Guizhou Wang – PersonEntity: Name: NameFull: Lei Feng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2023 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa Titles: – TitleFull: Remote Sensing, Vol 15, Iss 4, p 1084 (2023 Type: main |
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