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Open AccessLetter

A Data-Driven Approach to Classifying Wave Breaking in Infrared Imagery

School of Earth & Sustainability, Northern Arizona University, Flagstaff, AZ 86011, USA
Applied Physics Laboratory, University of Washington, Seattle, WA 98105, USA
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 859;
Received: 28 March 2019 / Revised: 1 April 2019 / Accepted: 5 April 2019 / Published: 10 April 2019
(This article belongs to the Special Issue AI-based Remote Sensing Oceanography)
We apply deep convolutional neural networks (CNNs) to estimate wave breaking type (e.g., non-breaking, spilling, plunging) from close-range monochrome infrared imagery of the surf zone. Image features are extracted using six popular CNN architectures developed for generic image feature extraction. Logistic regression on these features is then used to classify breaker type. The six CNN-based models are compared without and with augmentation, a process that creates larger training datasets using random image transformations. The simplest model performs optimally, achieving average classification accuracies of 89% and 93%, without and with image augmentation respectively. Without augmentation, average classification accuracies vary substantially with CNN model. With augmentation, sensitivity to model choice is minimized. A class activation analysis reveals the relative importance of image features to a given classification. During its passage, the front face and crest of a spilling breaker are more important than the back face. For a plunging breaker, the crest and back face of the wave are most important, which suggests that CNN-based models utilize the distinctive ‘streak’ temperature patterns observed on the back face of plunging breakers for classification. View Full-Text
Keywords: wave breaking; remote sensing; surf zone; machine learning wave breaking; remote sensing; surf zone; machine learning
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Buscombe, D.; Carini, R.J. A Data-Driven Approach to Classifying Wave Breaking in Infrared Imagery. Remote Sens. 2019, 11, 859.

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