Skip to main content

Research Repository

Advanced Search

UAV-based training for fully fuzzy classification of Sentinel-2 fluvial scenes

Carbonneau, P.E.; Belletti, B.; Micotti, M.; Lastoria, B.; Casaioli, M.; Mariani, S.; Marchetti, G.; Bizzi, S.

UAV-based training for fully fuzzy classification of Sentinel-2 fluvial scenes Thumbnail


Authors

B. Belletti

M. Micotti

B. Lastoria

M. Casaioli

S. Mariani

G. Marchetti

S. Bizzi



Abstract

An estimated 76% of global stream area is occupied by channels with widths above 30 meters. Sentinel‐2 imagery with resolutions of 10m could supply information about the composition of river corridors at national and global scales. Fuzzy classification models that infer sub‐pixel composition could further be used to compensate for small channel widths imaged at 10m of spatial resolution. A major challenge to this approach is the acquisition of suitable training data useable in machine learning models that can predict land‐cover type information from image radiance values. In this contribution, we present a method which combines unmanned aerial vehicles (UAV) and Sentinel‐2 imagery in order to develop a fuzzy classification approach capable of large‐scale investigations. Our approach uses hyperspatial UAV imagery in order to derive high resolution class information that can be used to train fuzzy classification models for Sentinel‐2 data where all bands are super‐resolved to a spatial resolution of 10 meters. We use a multi‐temporal UAV dataset covering an area of 5.25 km2. Using a novel Convolutional Neural Network (CNN) classifier, we predict sub‐pixel membership for Sentinel‐2 pixels in the fluvial corridor as divided into classes of water, vegetation and dry sediment. Our CNN model can predict fuzzy class memberships with median errors from ‐5% to +3% and mean absolute errors from 10% to 20%. We also show that our CNN fuzzy predictor can be used to predict crisp classes with accuracies from 95.5% to 99.9%. Finally, we use an example to show how a fuzzy CNN model trained with localised UAV data can be applied to longer channel reaches and detect new vegetation growth. We therefore argue that the novel use of UAVs as field validation tools for freely available satellite data can bridge the scale gap between local and regional fluvial studies

Citation

Carbonneau, P., Belletti, B., Micotti, M., Lastoria, B., Casaioli, M., Mariani, S., …Bizzi, S. (2020). UAV-based training for fully fuzzy classification of Sentinel-2 fluvial scenes. Earth Surface Processes and Landforms, 45(13), 3120-3140. https://doi.org/10.1002/esp.4955

Journal Article Type Article
Acceptance Date Jul 3, 2020
Online Publication Date Aug 18, 2020
Publication Date 2020-10
Deposit Date Jul 9, 2020
Publicly Available Date Mar 29, 2024
Journal Earth Surface Processes and Landforms
Print ISSN 0197-9337
Electronic ISSN 1096-9837
Publisher British Society for Geomorphology
Peer Reviewed Peer Reviewed
Volume 45
Issue 13
Pages 3120-3140
DOI https://doi.org/10.1002/esp.4955

Files



Published Journal Article (46.3 Mb)
PDF

Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
© 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.





You might also like



Downloadable Citations