Comparisons between machine learning and optimal transport-based approaches in classifying images are made in underwater orbital angular momentum (OAM) communications. A model is derived that justifies optimal transport for use in attenuated water environments. OAM pattern demultiplexing is performed using optimal transport and deep neural networks and compared to each other. Additionally, some of the complications introduced by signal attenuation are highlighted. The Radon cumulative distribution transform (R-CDT) is applied to OAM patterns to transform them to a linear subspace. The original OAM images and the R-CDT transformed patterns are used in several classification algorithms, and results are compared. The selected classification algorithms are the nearest subspace algorithm, a shallow convolutional neural network (CNN), and a deep neural network. It is shown that the R-CDT transformed images are more accurate than the original OAM images in pattern classification. Also, the nearest subspace algorithm performs better than the selected CNNs in OAM pattern classification in underwater environments.

中文翻译：

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水下 OAM 通信中基于传输的模式识别与深度神经网络

在水下轨道角动量 (OAM) 通信中对机器学习和基于最佳传输的图像分类方法进行了比较。推导出了一个模型，该模型证明了在弱水环境中使用的最佳传输是合理的。OAM 模式解复用使用最优传输和深度神经网络执行并相互比较。此外，还强调了信号衰减引入的一些复杂情况。氡累积分布变换 (R-CDT) 应用于 OAM 模式以将它们转换为线性子空间。原始 OAM 图像和 R-CDT 变换模式用于几种分类算法，并比较结果。选择的分类算法是最近子空间算法，浅层卷积神经网络（CNN），和一个深度神经网络。结果表明，R-CDT 变换后的图像在模式分类方面比原始 OAM 图像更准确。此外，最近子空间算法在水下环境中的 OAM 模式分类中比选定的 CNN 表现更好。