Siamese Networks with Transfer Learning for Change Detection in Sentinel-2 Images

2022
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Authors: Giuseppina Andresini; Annalisa Appice; Domenico Dell’Olio; Donato Malerba
Published in: AIxIA 2021 – Advances in Artificial Intelligence
Publisher: Springer International Publishing

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Abstract

The chapter delves into the application of Siamese Networks with Transfer Learning for Change Detection in Sentinel-2 Images. It introduces a semi-supervised method called ATLANTIS, which combines supervised and unsupervised learning steps to detect changes in bi-temporal optical images. The Siamese Network is initially trained on labeled images and then fine-tuned using unlabeled data, making it highly effective in scenarios with limited labeled data. The method is evaluated on datasets from Las Vegas and Cupertino, demonstrating significant improvements in accuracy and efficiency compared to purely supervised and unsupervised approaches. The chapter also discusses the potential of this method in various remote sensing applications, including urban planning, environmental monitoring, and disaster assessment.

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Title: Siamese Networks with Transfer Learning for Change Detection in Sentinel-2 Images
Authors: Giuseppina Andresini
Annalisa Appice
Domenico Dell’Olio
Donato Malerba
Publisher: Springer International Publishing
Book: AIxIA 2021 – Advances in Artificial Intelligence
Print ISBN: 978-3-031-08420-1

Electronic ISBN: 978-3-031-08421-8

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-031-08421-8_33

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