Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms

Google Earth Engine (GEE) is a versatile cloud platform in which pixel-based (PB) and object-oriented (OO) Land Use–Land Cover (LULC) classification approaches can be implemented, thanks to the availability of the many state-of-art functions comprising various Machine Learning (ML) algorithms. OO ap...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 12; H. 22; S. 3776
Hauptverfasser:	Tassi, Andrea, Vizzari, Marco
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 17.11.2020
Schlagworte:	Accuracy Algorithms area Classification Cloud computing Clustering Computer applications crops data collection decision support systems environment Google Earth Engine (GEE) information Internet Italy Iterative methods Lakes Land cover Land use land use and land cover maps land use land cover Landsat Landsat 8 Landsat satellites Learning algorithms Machine learning Mathematical analysis Object oriented programming Parameters PlanetScope principal component analysis Principal components analysis reliability Remote sensing Satellites Segmentation Sentinel 2 SNIC Software Statistical analysis Statistics Support vector machines surfaces testing Tuning Italy
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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