Evaluating capabilities of machine learning algorithms for aquatic vegetation classification in temperate wetlands using multi-temporal Sentinel-2 data
Different perspectives use of machine learning (ML) algorithms have proven their performance depends on the quality of reference data. This is particularly true when targets are complex environments, such as wetlands, on which a vast majority of studies are site-specific and based on a single date....
| Váldodahkkit: | , |
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| Materiálatiipa: | Artihkal |
| Giella: | English |
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Elsevier
2023-03-01
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| Ráidu: | International Journal of Applied Earth Observations and Geoinformation |
| Fáttát: | |
| Liŋkkat: | http://www.sciencedirect.com/science/article/pii/S1569843223000249 |