Cloud detection holds significant importance when analyzing satellite imagery, notably in the visible domain. A wide variety of detection tools already exists for this type of application, using radiometric information. With the aim of enhancing cloud detection, recent advances in deep learning have made it possible to create tools based on pattern recognition while leveraging radiometric data. This paper aims to present a method focused on machine learning, giving details of its construction process, from the creation of the datasets to overall performance. A sample use case is also presented to demonstrate the promising outcomes obtained. Using over 600,000km2 of ground-labeled satellite imagery and a U-Net based architecture for our machine learning algorithm, we achieved encouraging performances over various land types. Our results showed significant results when evaluated on the three most frequently used metrics for Image Segmentation. Our product gives interesting results in certain areas that present challenging ground types, such as snowy tops, when using Infrared imagery.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.