Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Deep Learning of High-Resolution Aerial Imagery for Coastal Marsh Change Detection: A Comparative Study

Version 1 : Received: 2 December 2021 / Approved: 6 December 2021 / Online: 6 December 2021 (13:31:08 CET)

A peer-reviewed article of this Preprint also exists.

Morgan, G.R.; Wang, C.; Li, Z.; Schill, S.R.; Morgan, D.R. Deep Learning of High-Resolution Aerial Imagery for Coastal Marsh Change Detection: A Comparative Study. ISPRS Int. J. Geo-Inf. 2022, 11, 100. Morgan, G.R.; Wang, C.; Li, Z.; Schill, S.R.; Morgan, D.R. Deep Learning of High-Resolution Aerial Imagery for Coastal Marsh Change Detection: A Comparative Study. ISPRS Int. J. Geo-Inf. 2022, 11, 100.

Journal reference: ISPRS Int. J. Geo-Inf. 2022, 11, 100
DOI: 10.3390/ijgi11020100

Abstract

Deep learning techniques are increasingly being recognized as effective image classifiers. Aside from their successful performance in past studies, the accuracies have varied in complex environments in comparison with the popularly applied machine learning classifiers. This study seeks to explore the feasibility for using a U-Net deep learning architecture to classify bi-temporal high resolution county scale aerial images to determine the spatial extent and changes of land cover classes that directly or indirectly impact tidal marsh. The image set used in the analysis is a collection of a 1-m resolution collection of National Agriculture Imagery Program (NAIP) tiles from 2009 and 2019 covering Beaufort County, South Carolina. The U-net CNN classification results were compared with two machine learning classifiers, the Random Trees (RT) and the Support Vector Machine (SVM). The results revealed a significant accuracy advantage in using the U-Net classifier (92.4%) as opposed to the SVM (81.6%) and RT (75.7%) classifiers for overall accuracy. From the perspective of a GIS analyst or coastal manager, the U-Net classifier is now an easily accessible nad powerful tool for mapping large areas. Change detection analysis indicated little areal change on marsh extent, though increased land development throughout the county has the potential to negatively impact the health of the marshes. Future work should explore applying the constructed U-Net classifier to coastal environments in large geographic areas, while also implementing other data sources (e.g., LIDAR, multispectral data) to enhance classification accuracy.

Keywords

Deep Learning; Machine Learning; Change Detection; Coastal; Marsh; Remote Sensing; Aerial Imagery

Subject

EARTH SCIENCES, Geoinformatics

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.