An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images
An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images
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Author / Creator
Publisher
MDPI AG
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Language
English
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Publisher
MDPI AG
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Contents
Tree species classification is important for the management and sustainable development of forest resources. Traditional object-oriented tree species classification methods, such as support vector machines, require manual feature selection and generally low accuracy, whereas deep learning technology can automatically extract image features to achie...
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Full title
An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images
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TN_cdi_doaj_primary_oai_doaj_org_article_d5d4a8f120f7441590871640c33bfddc
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_d5d4a8f120f7441590871640c33bfddc
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ISSN
2072-4292
E-ISSN
2072-4292
DOI
10.3390/rs12071128
