Ensemble Machine Learning on the Fusion of Sentinel Time Series Imagery with High-Resolution Orthoimagery for Improved Land Use/Land Cover Mapping

In the United States, several land use and land cover (LULC) data sets are available based on satellite data, but these data sets often fail to accurately represent features on the ground. Alternatively, detailed mapping of heterogeneous landscapes for informed decision-making is possible using high...

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Detaylı Bibliyografya
Asıl Yazarlar:	Mukti Ram Subedi, Carlos Portillo-Quintero, Nancy E. McIntyre, Samantha S. Kahl, Robert D. Cox, Gad Perry, Xiaopeng Song
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	MDPI AG 2024-07-01
Seri Bilgileri:	Remote Sensing
Konular:	bagging boosting stacking GEOBIA autocorrelation target-oriented cross-validation
Online Erişim:	https://www.mdpi.com/2072-4292/16/15/2778
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Ensemble Machine Learning on the Fusion of Sentinel Time Series Imagery with High-Resolution Orthoimagery for Improved Land Use/Land Cover Mapping

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