Transferring deep learning models for cloud detection between Landsat-8 and Proba-V

Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical properties of the acquired signals and propose a simple transfer learning approach using Landsat-8 and Proba-V sensors, whose images have different but similar spatial and spectral characteristics. Three types of experiments are conducted to demonstrate that transfer learning can work in both directions: (a) from Landsat-8 to Proba-V, where we show that models trained only with Landsat-8 data produce cloud masks 5 points more accurate than the current operational Proba-V cloud masking method, (b) from Proba-V to Landsat-8, where models that use only Proba-V data for training have an accuracy similar to the operational FMask in the publicly available Biome dataset (87.79-89.77% vs 88.48%), and (c) jointly from Proba-V and Landsat-8 to Proba-V, where we demonstrate that using jointly both data sources the accuracy increases 1-10 points when few Proba-V labeled images are available. These results highlight that, taking advantage of existing publicly available cloud masking labeled datasets, we can create accurate deep learning based cloud detection models for new satellites, but without the burden of collecting and labeling a large dataset of images.