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Multitemporal Cloud Masking in the Google Earth Engine
- Source :
- Remote Sensing, Volume 10, Issue 7, Pages: 1079, Remote Sensing, Vol 10, Iss 7, p 1079 (2018)
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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.
- Subjects :
- Masking (art)
010504 meteorology & atmospheric sciences
Computer science
Science
Optical instrument
Real-time computing
0211 other engineering and technologies
Cloud detection
Cloud computing
02 engineering and technology
Earth observation satellite
01 natural sciences
law.invention
multitemporal analysis
law
Satellite image
Landsat-8
change detection
021101 geological & geomatics engineering
0105 earth and related environmental sciences
business.industry
Google Earth Engine (GEE)
cloud masking
Power (physics)
General Earth and Planetary Sciences
business
image time series
Change detection
Subjects
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