6533b822fe1ef96bd127cb8e

RESEARCH PRODUCT

Domain Adaptation of Landsat-8 and Proba-V Data Using Generative Adversarial Networks for Cloud Detection

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Training machine learning algorithms for new satellites requires collecting new data. This is a critical drawback for most remote sensing applications and specially for cloud detection. A sensible strategy to mitigate this problem is to exploit available data from a similar sensor, which involves transforming this data to resemble the new sensor data. However, even taking into account the technical characteristics of both sensors to transform the images, statistical differences between data distributions still remain. This results in a poor performance of the methods trained on one sensor and applied to the new one. In this this work, we propose to use the generative adversarial networks (GANs) framework to adapt the data from the new satellite. In particular, we use Landsat-8 images, with the corresponding ground truth, to perform cloud detection in Proba-V. Results show that the GANs adaptation significantly improves the detection accuracy.