Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V Images for Cloud Detection

نویسندگان

چکیده

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 accuracy, which hampers knowledge information sharing across sensors. This particularly harmful machine learning algorithms, since gathering new ground-truth data train models costly requires experienced manpower. In this work, we propose a domain adaptation transformation reduce statistical differences between images two order boost performance transfer models. proposed methodology based on cycle consistent generative adversarial framework that trains model an unpaired manner. particular, Landsat-8 Proba-V satellites, present different but compatible spatio-spectral characteristics, used illustrate method. obtained significantly reduces image datasets while preserving spatial spectral adapted images, is, hence, useful any general purpose cross-sensor application. addition, training can be modified improve specific remote sensing application, such as cloud detection, by including dedicated term cost function. Results show that, when applied, detection trained increase accuracy Proba-V.

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ژورنال

عنوان ژورنال: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

سال: 2021

ISSN: ['2151-1535', '1939-1404']

DOI: https://doi.org/10.1109/jstars.2020.3031741