  The number of Earth observation satellites carrying optical sensors with
similar characteristics is constantly growing. Despite their similarities and
the potential synergies among them, derived satellite products are often
developed for each sensor independently. Differences in retrieved radiances
lead to significant drops in accuracy, which hampers knowledge and information
sharing across sensors. This is particularly harmful for machine learning
algorithms, since gathering new ground truth data to train models for each
sensor is costly and requires experienced manpower. In this work, we propose a
domain adaptation transformation to reduce the statistical differences between
images of two satellite sensors in order to boost the performance of transfer
learning models. The proposed methodology is based on the Cycle Consistent
Generative Adversarial Domain Adaptation (CyCADA) framework that trains the
transformation model in an unpaired manner. In particular, Landsat-8 and
Proba-V satellites, which present different but compatible spatio-spectral
characteristics, are used to illustrate the method. The obtained transformation
significantly reduces differences between the image datasets while preserving
the spatial and spectral information of adapted images, which is hence useful
for any general purpose cross-sensor application. In addition, the training of
the proposed adversarial domain adaptation model can be modified to improve the
performance in a specific remote sensing application, such as cloud detection,
by including a dedicated term in the cost function. Results show that, when the
proposed transformation is applied, cloud detection models trained in Landsat-8
data increase cloud detection accuracy in Proba-V.
