A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping

By Patrick Griffiths, Sebastian van der Linden, Tobias Kuemmerle, Patrick Hostert. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013

305 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/029f4019a36b2265dfe1d39968cfa916f9e1683f

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1. Verena Kuch
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Citations in the corpus

• Land Cover Classification using Google Earth Engine and Random Forest Classifier - The Role of Image Composition (2020, background): “, atmospheric correction), the effect of different footprints or different acquisition dates [52].”
• Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms (2020, methodology): “In this regard, GEE allows the users to define different modus operandi for the combination of the input data, allowing to efficiently create light, cloud-free, multi-temporal, composite dataset without having to run into blocks related to limited local computational resources images [5,6].”