Two neural networks are combined to detect wakes in synthetic aperture radar (SAR) images of the ocean. The first network detects local wake features in smaller sub-proportions of the image, and the second network integrates the information from the first network to determine the presence or absence of a wake in the entire image. The networks train directly using the gradient descent method on either real SAR images or on synthetic images and are designed to detect wakes in images with low signal-to-noise ratios. When trained on real images, the network detector recognizes the wake in any translation and is robust with respect to rotations. With synthetic images, the network model is able to recognize wakes with all possible translations, rotations and over a wide range of opening angles.
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