Beach State Recognition Using Argus Imagery and Convolutional Neural Networks

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 23; p. 3953
• Main Authors: Ellenson, Ashley N., Simmons, Joshua A., Wilson, Greg W., Hesser, Tyler J., Splinter, Kristen D.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2020
• Subjects: Algorithms; Aquatic birds; Argus; artificial intelligence; Artificial neural networks; Bathymeters; Bathymetry; beach state; Beaches; Cameras; Classification; CNN; Coasts; Cyclones; Datasets; deep learning; ducks; Energy; Grain size; Image classification; information; Labeling; Laboratories; Learning algorithms; Machine learning; Measurement techniques; Model testing; Morphology; Nearshore circulation; nearshore morphology; Neural networks; New South Wales; North Carolina; R&D; Remote sensing; Research & development; Sediments; Self tests; Soil erosion; Swimming; Terraces; testing; Topography; Wave breaking; Wave power; North Carolina; United States > US; New South Wales
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12233953

Nearshore morphology is a key driver in wave breaking and the resulting nearshore circulation, recreational safety, and nutrient dispersion. Morphology persists within the nearshore in specific shapes that can be classified into equilibrium states. Equilibrium states convey qualitative information about bathymetry and relevant physical processes. While nearshore bathymetry is a challenge to collect, much information about the underlying bathymetry can be gained from remote sensing of the surfzone. This study presents a new method to automatically classify beach state from Argus daytimexposure imagery using a machine learning technique called convolutional neural networks (CNNs). The CNN processed imagery from two locations: Narrabeen, New South Wales, Australia and Duck, North Carolina, USA. Three different CNN models are examined, one trained at Narrabeen, one at Duck, and one trained at both locations. Each model was tested at the location where it was trained in a self-test, and the single-beach models were tested at the location where it was not trained in a transfer-test. For the self-tests, skill (as measured by the F-score) was comparable to expert agreement (CNN F-values at Duck = 0.80 and Narrabeen = 0.59). For the transfer-tests, the CNN model skill was reduced by 24–48%, suggesting the algorithm requires additional local data to improve transferability performance. Transferability tests showed that comparable F-scores (within 10%) to the self-trained cases can be achieved at both locations when at least 25% of the training data is from each site. This suggests that if applied to additional locations, a CNN model trained at one location may be skillful at new sites with limited new imagery data needed. Finally, a CNN visualization technique (Guided-Grad-CAM) confirmed that the CNN determined classifications using image regions (e.g., incised rip channels, terraces) that were consistent with beach state labelling rules.