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Semi-Supervised Detection of Detailed Ground Feature Changes and Its Impact on Land Surface Temperature

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Titel: Semi-Supervised Detection of Detailed Ground Feature Changes and Its Impact on Land Surface Temperature
Autoren: Pinghao Wu, Jiacheng Liang, Jianhui Xu, Kaiwen Zhong, Hongda Hu, Jian Zuo
Quelle: Atmosphere, Vol 14, Iss 12, p 1813 (2023)
Verlagsinformationen: MDPI AG
Publikationsjahr: 2023
Bestand: Directory of Open Access Journals: DOAJ Articles
Schlagwörter: semi-supervised detection, detailed ground feature changes, Deeplab V3+, LST, spatiotemporal heterogeneity, Meteorology. Climatology, QC851-999
Beschreibung: This paper presents a semi-supervised change detection optimization strategy as a means to mitigate the reliance of unsupervised/semi-supervised algorithms on pseudo-labels. The benefits of the Class-balanced Self-training Framework (CBST) and Deeplab V3+ were exploited to enhance classification accuracy for further analysis of microsurface land surface temperature (LST), as indicated by the change detection difference map obtained using iteratively reweighted multivariate alteration detection (IR-MAD). The evaluation statistics revealed that the DE_CBST optimization scheme achieves superior change detection outcomes. In comparison to the results of Deeplab V3+, the precision indicator demonstrated a 2.5% improvement, while the commission indicator exhibited a reduction of 2.5%. Furthermore, when compared to those of the CBST framework, the F1 score showed a notable enhancement of 6.3%, and the omission indicator exhibited a decrease of 8.9%. Moreover, DE_CBST optimization improves the identification accuracy of water in unchanged areas on the basis of Deeplab V3+ classification results and significantly improves the classification effect on bare land in changed areas on the basis of CBST classification results. In addition, the following conclusions are drawn from the discussion on the correlation between ground object categories and LST on a fine-scale: (1) the correlation between land use categories and LST all have good results in GTWR model fitting, which shows that local LST has a high correlation with the corresponding range of the land use category; (2) the changes of the local LST were generally consistent with the changes of the overall LST, but the evolution of the LST in different regions still has a certain heterogeneity, which might be related to the size of the local LST region; and (3) the local LST and the land use category of the corresponding grid cells did not show a completely consistent correspondence relationship. When discussing the local LST, it is necessary to consider the change in the ...
Publikationsart: article in journal/newspaper
Sprache: English
Relation: https://www.mdpi.com/2073-4433/14/12/1813; https://doaj.org/toc/2073-4433; https://doaj.org/article/5b099e4804e3414e8040b1923bd03945
DOI: 10.3390/atmos14121813
Verfügbarkeit: https://doi.org/10.3390/atmos14121813
https://doaj.org/article/5b099e4804e3414e8040b1923bd03945
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Semi-Supervised Detection of Detailed Ground Feature Changes and Its Impact on Land Surface Temperature
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Pinghao+Wu%22">Pinghao Wu</searchLink><br /><searchLink fieldCode="AR" term="%22Jiacheng+Liang%22">Jiacheng Liang</searchLink><br /><searchLink fieldCode="AR" term="%22Jianhui+Xu%22">Jianhui Xu</searchLink><br /><searchLink fieldCode="AR" term="%22Kaiwen+Zhong%22">Kaiwen Zhong</searchLink><br /><searchLink fieldCode="AR" term="%22Hongda+Hu%22">Hongda Hu</searchLink><br /><searchLink fieldCode="AR" term="%22Jian+Zuo%22">Jian Zuo</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Atmosphere, Vol 14, Iss 12, p 1813 (2023)
– Name: Publisher
  Label: Publisher Information
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  Data: MDPI AG
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2023
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  Group: HoldingsInfo
  Data: Directory of Open Access Journals: DOAJ Articles
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  Data: <searchLink fieldCode="DE" term="%22semi-supervised+detection%22">semi-supervised detection</searchLink><br /><searchLink fieldCode="DE" term="%22detailed+ground+feature+changes%22">detailed ground feature changes</searchLink><br /><searchLink fieldCode="DE" term="%22Deeplab+V3%2B%22">Deeplab V3+</searchLink><br /><searchLink fieldCode="DE" term="%22LST%22">LST</searchLink><br /><searchLink fieldCode="DE" term="%22spatiotemporal+heterogeneity%22">spatiotemporal heterogeneity</searchLink><br /><searchLink fieldCode="DE" term="%22Meteorology%2E+Climatology%22">Meteorology. Climatology</searchLink><br /><searchLink fieldCode="DE" term="%22QC851-999%22">QC851-999</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: This paper presents a semi-supervised change detection optimization strategy as a means to mitigate the reliance of unsupervised/semi-supervised algorithms on pseudo-labels. The benefits of the Class-balanced Self-training Framework (CBST) and Deeplab V3+ were exploited to enhance classification accuracy for further analysis of microsurface land surface temperature (LST), as indicated by the change detection difference map obtained using iteratively reweighted multivariate alteration detection (IR-MAD). The evaluation statistics revealed that the DE_CBST optimization scheme achieves superior change detection outcomes. In comparison to the results of Deeplab V3+, the precision indicator demonstrated a 2.5% improvement, while the commission indicator exhibited a reduction of 2.5%. Furthermore, when compared to those of the CBST framework, the F1 score showed a notable enhancement of 6.3%, and the omission indicator exhibited a decrease of 8.9%. Moreover, DE_CBST optimization improves the identification accuracy of water in unchanged areas on the basis of Deeplab V3+ classification results and significantly improves the classification effect on bare land in changed areas on the basis of CBST classification results. In addition, the following conclusions are drawn from the discussion on the correlation between ground object categories and LST on a fine-scale: (1) the correlation between land use categories and LST all have good results in GTWR model fitting, which shows that local LST has a high correlation with the corresponding range of the land use category; (2) the changes of the local LST were generally consistent with the changes of the overall LST, but the evolution of the LST in different regions still has a certain heterogeneity, which might be related to the size of the local LST region; and (3) the local LST and the land use category of the corresponding grid cells did not show a completely consistent correspondence relationship. When discussing the local LST, it is necessary to consider the change in the ...
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  Data: https://www.mdpi.com/2073-4433/14/12/1813; https://doaj.org/toc/2073-4433; https://doaj.org/article/5b099e4804e3414e8040b1923bd03945
– Name: DOI
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  Data: 10.3390/atmos14121813
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  Data: https://doi.org/10.3390/atmos14121813<br />https://doaj.org/article/5b099e4804e3414e8040b1923bd03945
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        Value: 10.3390/atmos14121813
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: semi-supervised detection
        Type: general
      – SubjectFull: detailed ground feature changes
        Type: general
      – SubjectFull: Deeplab V3+
        Type: general
      – SubjectFull: LST
        Type: general
      – SubjectFull: spatiotemporal heterogeneity
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      – SubjectFull: Meteorology. Climatology
        Type: general
      – SubjectFull: QC851-999
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    Titles:
      – TitleFull: Semi-Supervised Detection of Detailed Ground Feature Changes and Its Impact on Land Surface Temperature
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            – TitleFull: Atmosphere, Vol 14, Iss 12, p 1813 (2023
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