A deep learning method for optimizing semantic segmentation accuracy of remote sensing images based...
A deep learning method for optimizing semantic segmentation accuracy of remote sensing images based on improved UNet
About this item
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Author / Creator
Wang, Xiaolei , Hu, Zirong , Shi, Shouhai , Hou, Mei , Xu, Lei and Zhang, Xiang
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
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Publication information
Publisher
London: Nature Publishing Group UK
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Scope and Contents
Contents
Semantic segmentation of remote sensing imagery (RSI) is critical in many domains due to the diverse landscapes and different sizes of geo-objects that RSI contains, making semantic segmentation challenging. In this paper, a convolutional network, named Adaptive Feature Fusion UNet (AFF-UNet), is proposed to optimize the semantic segmentation perfo...
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Full title
A deep learning method for optimizing semantic segmentation accuracy of remote sensing images based on improved UNet
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_ea820bcce80442b2925d81dcccdecc25
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_ea820bcce80442b2925d81dcccdecc25
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-023-34379-2
