从同步卫星图像中自动探测墨西哥湾流北壁

A. Gangopadhyay, Kevin Lydon, Jeffrey A. Rezendes, R. Balasubramanian, I. Valova
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引用次数: 3

摘要

开发计算方法来自动识别墨西哥湾流北壁(GSNW)和海洋中类似的洋流是许多类型的业务海洋模型的长期需求。具体而言,面向特征的区域建模系统需要定期对GSNW和环(涡)进行精确的数字化。确定其位置和边界的典型方法需要熟练的人工操作员进行耗时的可视化特征提取。这些专家正在执行一项特征提取任务,该任务可以自动化,以节省时间,保证客观性,并可能提高精度。在本文中,我们提出了解决这一问题的两种独立方法的初步结果。在其中一种方法,即动态方法中,该方法首先找到最可能的等海面高度等高线的边界,墨西哥湾流北壁可能落在其中。其他特征,如漩涡,也被捕获,将在一轮形状分析后搁置一边。等高等高线上的任何间隙都由不同高度的斜坡组合而成的线段填充。第二种是机器学习方法,在GSNW数据集上使用人工神经网络,该数据集在过去六年中(2009-2015年)由分析师每周生成。人工神经网络是一种学习算法,被设计为神经元之间的连接系统。该神经网络将首先使用分析师指定的GSNW路径来确定径向基函数的神经权值。然后,该网络将使用用于识别这些线路的同步海面高度和温度数据,并训练自己开发一个智能网络,该网络将能够在几乎没有人为干预的情况下,自行从并发卫星图像中识别GSNW路径。从长远来看,我们希望将这两种技术合并为一个独特而统一的结构,以用于操作。该方法的一般方法有可能用于其他类似的具有数据同化的数值模型系统的操作建模、再分析和技能评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards an automated detection of the Gulf Stream North Wall from concurrent satellite images
Developing computational methods to automatically identify the Gulf Stream North Wall (GSNW) and similar currents in the ocean is a long-standing need for many types of operational ocean models. Specifically, the Feature-Oriented regional modeling system requires an accurate digitization of the GSNW and Rings (eddies) on a regular basis. Typical methods to determine its position and boundaries require skilled human operators to do a time-consuming manual extraction of visualized features. These experts are performing a feature extraction task that can be automated to save time, guarantee objectivity, and potentially increase precision.In this paper we present first-results from two independent approaches of addressing this issue. In one of the approaches, the dynamical approach, the methodology begins by finding the most-likely bounds of iso-sea-surface-height contours within which the Gulf Stream north wall might fall. Other features, such as eddies, which are also captured, will be set aside after a round of shape analysis. Any gap in the isoheight contours is filled with segments that are generated by combining the slopes from different heights.The second, a machine-learning approach uses an artificial neural network over a GSNW dataset, which has been generated weekly over past six years (2009-2015) by analysts. An artificial neural network is a type of learning algorithm designed as a system of neurons with connections among them. This neural network will first use the analyst-designated GSNW paths to determine the neural weights of the radial basis functions. Then the network will use the concurrent sea-surface height and temperature data that were used to identify those lines, and train itself to develop a smart network which will be able to identify GSNW paths from the concurrent satellite images on its own, with little to no human intervention.In the long-term, we expect to merge the two techniques in a unique and unifying construct to be used operationally. A general approach of this methodology has the potential of being used for other similar operational modeling, reanalysis and skill assessment of numerical model system with data assimilation.
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