Details

Autor(en) / Beteiligte

• Zaytar, Mohamed Akram
• El Amrani, Chaker

Titel

Satellite Imagery Noising With Generative Adversarial Networks

Ist Teil von

• International journal of cognitive informatics & natural intelligence, 2021-01, Vol.15 (1), p.16-25

Ort / Verlag

Hershey: IGI Global

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Using satellite imagery and remote sensing data for supervised and self-supervised learning problems can be quite challenging when parts of the underlying datasets are missing due to natural phenomena (clouds, fog, haze, mist, etc.). Solving this problem will improve remote sensing data augmentation and make use of it in a world where satellite imagery represents a great resource to exploit in any big data pipeline setup. In this paper, the authors present a generative adversarial network (GANs) model that can generate natural atmospheric noise that serves as a data augmentation preprocessing tool to produce input to supervised machine learning algorithms.