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.

中文翻译：

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使用阿格斯影像和卷积神经网络的海滩状态识别

近岸形态是破坏海浪以及由此产生的近岸环流，娱乐安全和养分扩散的关键驱动力。形态在近岸以特定形状持续存在，可以分为平衡状态。平衡状态传达有关测深和相关物理过程的定性信息。尽管收集近海测深图是一项挑战，但可以通过对海浪区进行遥感来获取有关基础测深仪的许多信息。这项研究提出了一种从Argus daytimexposure自动分类海滩状态的新方法使用称为卷积神经网络（CNN）的机器学习技术对图像进行成像。CNN处理了来自两个地点的图像：澳大利亚新南威尔士州的纳拉宾和美国北卡罗来纳州的鸭子。审查了三种不同的CNN模型，一种在Narrabeen接受培训，一种在Duck接受培训，另一种在两个地点接受培训。每个模型都在自测训练的位置进行了测试，而单海滩模型在传递测试未训练的位置进行了测试。对于自测，技能（通过F分数衡量）与专家协议相当（Duck = 0.80和Narrabeen = 0.59的CNN F值）。对于传输测试，CNN模型的技能降低了24–48％，这表明该算法需要更多的本地数据以提高传输性能。可传递性测试表明，当至少25％的训练数据来自每个站点时，在两个位置都可以实现与自我训练情况相当的F分数（在10％以内）。这表明，如果将其应用于其他位置，则在一个位置训练的CNN模型在需要有限新图像数据的新位置可能会熟练。最后，CNN可视化技术（Guided-Grad-CAM）确认CNN使用与海滩州标签规则一致的图像区域（例如，切开的裂隙通道，阶地）确定分类。