  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.
