Abstract

With the continuous development of space and sensor technologies during the last 40 years, ocean remote sensing has entered into the big-data era with typical five-V (volume, variety, value, velocity and veracity) characteristics. Ocean remote-sensing data archives reach several tens of petabytes and massive satellite data are acquired worldwide daily. To precisely, efficiently and intelligently mine the useful information submerged in such ocean remote-sensing data sets is a big challenge. Deep learning—a powerful technology recently emerging in the machine-learning field—has demonstrated its more significant superiority over traditional physical- or statistical-based algorithms for image-information extraction in many industrial-field applications and starts to draw interest in ocean remote-sensing applications. In this review paper, we first systematically reviewed two deep-learning frameworks that carry out ocean remote-sensing-image classifications and then presented eight typical applications in ocean internal-wave/eddy/oil-spill/coastal-inundation/sea-ice/green-algae/ship/coral-reef mapping from different types of ocean remote-sensing imagery to show how effective these deep-learning frameworks are. Researchers can also readily modify these existing frameworks for information mining of other kinds of remote-sensing imagery.

中文翻译：

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基于深度学习的海洋遥感影像信息挖掘

 抽象的

近40年来，随着空间技术和传感器技术的不断发展，海洋遥感进入了具有典型5V（体积、种类、价值、速度和准确性）特征的大数据时代。海洋遥感数据档案达到数十PB，全球每天都在获取海量卫星数据。如何精准、高效、智能地挖掘海洋遥感数据集中的有用信息是一个巨大的挑战。深度学习是机器学习领域最近兴起的一项强大技术，在许多工业领域应用中，在图像信息提取方面，它比传统的基于物理或统计的算法具有更显着的优势，并开始引起人们对海洋遥感的兴趣。传感应用。在这篇综述论文中，我们首先系统地回顾了两种进行海洋遥感图像分类的深度学习框架，然后提出了海洋内波/涡流/溢油/沿海淹没/海冰/来自不同类型海洋遥感图像的绿藻/船舶/珊瑚礁映射，以显示这些深度学习框架的有效性。研究人员还可以轻松修改这些现有框架，用于其他类型遥感图像的信息挖掘。