In the last 40 years, remote sensing technology has evolved, significantly advancing ocean observation and catapulting its data into the big data era. How to efficiently and accurately process and analyze ocean big data and solve practical problems based on ocean big data constitute a great challenge. Artificial intelligence (AI) technology has developed rapidly in recent years. Numerous deep learning (DL) models have emerged, becoming prevalent in big data analysis and practical problem solving. Among these, convolutional neural networks (CNNs) stand as a representative class of DL models and have established themselves as one of the premier solutions in various research areas, including computer vision and remote sensing applications. In this study, we first discuss the model architectures of CNNs and some of their variants as well as how they can be applied to the processing and analysis of ocean remote sensing data. Then, we demonstrate that CNNs can fulfill most of the requirements for ocean remote sensing applications across the following six categories: reconstruction of the 3D ocean field, information extraction, image superresolution, ocean phenomena forecast, transfer learning method, and CNN model interpretability method. Finally, we discuss the technical challenges facing the application of CNN-based ocean remote sensing big data and summarize future research directions.

中文翻译：

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DeepBlue：海洋遥感的高级卷积神经网络应用

在过去的 40 年里，遥感技术不断发展，极大地推进了海洋观测，并将其数据带入了大数据时代。如何高效、准确地处理和分析海洋大数据并基于海洋大数据解决实际问题构成了巨大的挑战。人工智能（AI）技术近年来发展迅速。许多深度学习（DL）模型已经出现，在大数据分析和实际问题解决中变得普遍。其中，卷积神经网络 (CNN) 是 DL 模型的代表类别，并已成为计算机视觉和遥感应用等各个研究领域的首要解决方案之一。在本研究中，我们首先讨论 CNN 的模型架构及其一些变体，以及如何将它们应用于海洋遥感数据的处理和分析。然后，我们证明 CNN 可以满足以下六类海洋遥感应用的大部分要求：3D 海洋场重建、信息提取、图像超分辨率、海洋现象预测、迁移学习方法和 CNN 模型可解释性方法。最后讨论了基于CNN的海洋遥感大数据应用面临的技术挑战并总结了未来的研究方向。