Evaluating capabilities of machine learning algorithms for aquatic vegetation classification in temperate wetlands using multi-temporal Sentinel-2 data

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....

书目详细资料
Main Authors:	Erika Piaser, Paolo Villa
格式:	文件
语言:	English
出版:	Elsevier 2023-03-01
丛编:	International Journal of Applied Earth Observations and Geoinformation
主题:	Supervised classification Wetland vegetation Spectral indices Random forest Support vector machine
在线阅读:	http://www.sciencedirect.com/science/article/pii/S1569843223000249

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