6533b82efe1ef96bd129339d

RESEARCH PRODUCT

Cloud detection machine learning algorithms for PROBA-V

Jordi Muñoz-maríLuis Gómez-chovaGonxalo Mateo-garciaGustau Camps-valls

subject

FOS: Computer and information sciencesComputer Science - Machine Learning010504 meteorology & atmospheric sciencesComputer scienceRemote sensing applicationFeature extraction0211 other engineering and technologiesFOS: Physical sciencesCloud computing02 engineering and technologyLand coverMachine learningcomputer.software_genre01 natural sciencesMachine Learning (cs.LG)Astrophysics::Galaxy Astrophysics021101 geological & geomatics engineering0105 earth and related environmental sciencesPixelbusiness.industrySupport vector machinePhysics - Atmospheric and Oceanic PhysicsAtmospheric and Oceanic Physics (physics.ao-ph)Artificial intelligencebusinesscomputerAlgorithm

description

This paper presents the development and implementation of a cloud detection algorithm for Proba-V. Accurate and automatic detection of clouds in satellite scenes is a key issue for a wide range of remote sensing applications. With no accurate cloud masking, undetected clouds are one of the most significant sources of error in both sea and land cover biophysical parameter retrieval. The objective of the algorithms presented in this paper is to detect clouds accurately providing a cloud flag per pixel. For this purpose, the method exploits the information of Proba-V using statistical machine learning techniques to identify the clouds present in Proba-V products. The effectiveness of the proposed method is successfully illustrated using a large number of real Proba-V images.

yearjournalcountryeditionlanguage
2020-12-092017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
https://doi.org/10.1109/igarss.2017.8127437