Article
Version 1
Preserved in Portico This version is not peer-reviewed
A Data-Driven Approach to Classifying Wave Breaking in Infrared Imagery
Version 1
: Received: 28 March 2019 / Approved: 29 March 2019 / Online: 29 March 2019 (12:16:01 CET)
A peer-reviewed article of this Preprint also exists.
Buscombe, D.; Carini, R.J. A Data-Driven Approach to Classifying Wave Breaking in Infrared Imagery. Remote Sens. 2019, 11, 859. Buscombe, D.; Carini, R.J. A Data-Driven Approach to Classifying Wave Breaking in Infrared Imagery. Remote Sens. 2019, 11, 859.
Abstract
We apply deep convolutional neural networks (CNNs) to estimate wave breaking type 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. Whereas for a plunging breaker, the crest and back face of the wave are most important. This suggests that CNN-based models utilize the distinctive `streak' temperature patterns observed on the back face of plunging breakers for classification.
Keywords
wave breaking; remote sensing; surf zone; machine learning
Subject
Environmental and Earth Sciences, Oceanography
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment