A Data-Driven Approach to Classifying Wave Breaking in Infrared Imagery
A Data-Driven Approach to Classifying Wave Breaking in Infrared Imagery
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DOI：
10.3390/rs11070859
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发表时间：
2019-04-01
期刊：
影响因子：
5
通讯作者：
Carini, Roxanne J.
中科院分区：
文献类型：
--
作者：
Buscombe, Daniel;Carini, Roxanne J.
我们应用深度卷积神经网络（CNN）来估计波浪破碎类型（例如，非破碎，溢出，暴跌）从近距离单色红外图像的冲浪区。图像特征提取使用六种流行的CNN架构开发的通用图像特征提取。然后使用对这些特征的逻辑回归来对断路器类型进行分类。这六个基于CNN的模型在没有增强和有增强的情况下进行了比较，增强是一个使用随机图像变换创建更大训练数据集的过程。最简单的模型表现最佳，实现平均分类准确率为89%和93%，分别没有和图像增强。在没有增强的情况下，CNN模型的平均分类准确率差异很大。随着增强，对模型选择的敏感性被最小化。类激活分析揭示了图像特征对给定分类的相对重要性。在溢流式破碎机的通过过程中，其前表面和顶部比后表面更重要。对于倾翻式破碎机，波峰和背面是最重要的，这表明基于CNN的模型利用在倾翻式破碎机背面观察到的独特条纹的温度模式进行分类。
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.