Integrated 1D, 2D, and 3D CNNs Enable Robust and Efficient Land Cover Classification from Hyperspect...
Integrated 1D, 2D, and 3D CNNs Enable Robust and Efficient Land Cover Classification from Hyperspectral Imagery
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Basel: MDPI AG
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English
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Basel: MDPI AG
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Convolutional neural networks (CNNs) have recently been demonstrated to be able to substantially improve the land cover classification accuracy of hyperspectral images. Meanwhile, the rapidly developing capacity for satellite and airborne image spectroscopy as well as the enormous archives of spectral data have imposed increasing demands on the com...
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Integrated 1D, 2D, and 3D CNNs Enable Robust and Efficient Land Cover Classification from Hyperspectral Imagery
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TN_cdi_doaj_primary_oai_doaj_org_article_a19708d4640f4d2c8982edf36e163ca1
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_a19708d4640f4d2c8982edf36e163ca1
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ISSN
2072-4292
E-ISSN
2072-4292
DOI
10.3390/rs15194797
