  Convolutional neural networks (CNN) have proven to be state of the art
methods for many image classification tasks and their use is rapidly increasing
in remote sensing problems. One of their major strengths is that, when enough
data is available, CNN perform an end-to-end learning without the need of
custom feature extraction methods. In this work, we study the use of different
CNN architectures for cloud masking of Proba-V multispectral images. We compare
such methods with the more classical machine learning approach based on feature
extraction plus supervised classification. Experimental results suggest that
CNN are a promising alternative for solving cloud masking problems.
