Residual U-Net with Attention for Detecting Clouds in Satellite Imagery

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.

Downloads

Download Preprint

Authors

Alexander L. De Souza , Parnia Shokri

Abstract

Semantic segmentation of clouds in Earth observation imagery is an important task in a variety of remote sensing contexts: from the application of atmospheric corrections to being able to accurately omit cloud pixels when extracting information about ground features. Here we introduce a deep learning approach based on the popular U-Net architecture. The core of the architecture is an U-Net with residual units that ease the training of the network. An attention mechanism is also incorporated to enable the model to more effectively learn and distinguish between cloud and non-cloud features. We also explore two complementary loss functions, Binary Cross Entropy and Jaccard, in order to overcome data imbalances common to this application. Our model is trained on a uniquely curated dataset spanning a wide variety of resolutions, scene contexts, lighting conditions, and seasonality. Our experiments demonstrate that this model is an accurate and robust model for the semantic segmentation of clouds in satellite imagery, and the model achieves state-of-the-art performance over many other models (including others based on CNN architectures) on common benchmark datasets, even without having been exclusively trained on images from the sources in those datasets.

DOI

https://doi.org/10.31223/X52D39

Subjects

Computer Sciences, Earth Sciences, Physical Sciences and Mathematics

Keywords

remote sensing, cloud detection, landsat-8, sentinel-2, Deep learning, U-Net, attention

Dates

Published: 2023-01-13 06:32

Last Updated: 2023-01-13 06:32

License

CC-By Attribution-ShareAlike 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
Datasets referenced in this work are available from the CMIX portal https://calvalportal.ceos.org/cmix-sites