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Multitemporal Cloud Masking in the Google Earth Engine

Authors :
Gustau Camps-Valls
Jordi Muñoz-Marí
Luis Gómez-Chova
Julia Amorós-López
Gonzalo Mateo-Garcia
Source :
Remote Sensing, Volume 10, Issue 7, Pages: 1079, Remote Sensing, Vol 10, Iss 7, p 1079 (2018)

Abstract

The exploitation of Earth observation satellite images acquired by optical instruments requires an automatic and accurate cloud detection. Multitemporal approaches to cloud detection are usually more powerful than their single scene counterparts since the presence of clouds varies greatly from one acquisition to another whereas surface can be assumed stationary in a broad sense. However, two practical limitations usually hamper their operational use: the access to the complete satellite image archive and the required computational power. This work presents a cloud detection and removal methodology implemented in the Google Earth Engine (GEE) cloud computing platform in order to meet these requirements. The proposed methodology is tested for the Landsat-8 mission over a large collection of manually labeled cloud masks from the Biome dataset. The quantitative results show state-of-the-art performance compared with mono-temporal standard approaches, such as FMask and ACCA algorithms, yielding improvements between 4–5% in classification accuracy and 3–10% in commission errors. The algorithm implementation within the Google Earth Engine and the generated cloud masks for all test images are released for interested readers.

Details

Language :
English
ISSN :
20724292
Volume :
10
Issue :
7
Database :
OpenAIRE
Journal :
Remote Sensing
Accession number :
edsair.doi.dedup.....f743443d52860ae77f4427d2e7d7adc6
Full Text :
https://doi.org/10.3390/rs10071079