Cloud detection machine learning algorithms for PROBA-V
Accurate cloud detection is crucial for remote sensing applications, as undetected clouds are a major source of error in biophysical parameter retrieval for sea and land cover.
This paper developed a cloud detection algorithm for Proba-V satellite images. It uses statistical machine learning techniques to identify clouds per pixel, successfully demonstrated on a large dataset of real Proba-V images.
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.