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

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 en-vironments, in comparison with the popularly of applied machine learning classifiers. This study seeks to explore the feasibility of 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 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 and powerful tool for mapping large areas. Change detection analysis indicated little areal change on marsh ex-tent, 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 classi-fier to coastal environments in large geographic areas, while also implementing other data sources (e.g., LIDAR and multispectral data) to enhance classification accuracy.