Detection of Moroccan coastal upwelling in SST images using the Expectation-Maximization

A. Tamim, K. Minaoui, K. Daoudi, A. Atillah, D. Aboutajdine
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Abstract

This paper proposes an unsupervised algorithm for automatic detection and segmentation of upwelling region in Moroccan Atlantic coast using the Sea Surface Temperature (SST) satellite images. This has been done by exploring the Expectation-Maximization algorithm. The good number of clusters that best reproduces the shape of upwelling areas is selected by using the two popular Davies-Bouldin and Dunn indices. Area opening technique is developed that is used to remove and discarded the residuals noise in offshore waters not belonging to the upwelling region. The complete system has been validated by an oceanographer using a database of 30 SST images of the year 2007, demonstrating its capability and robustness for precise detection of Moroccan coastal upwelling.
基于期望最大化的摩洛哥海温图像上升流检测
提出了一种利用海表温度(SST)卫星图像自动检测和分割摩洛哥大西洋沿岸上升流区域的无监督算法。这是通过探索期望最大化算法来实现的。通过使用两种流行的Davies-Bouldin和Dunn指数来选择最能再现上升流区域形状的簇的数量。开发了区域开放技术,用于去除和丢弃近海非上升流区域的残留噪声。一名海洋学家利用一个包含2007年30张海温图像的数据库对整个系统进行了验证,证明了它在精确探测摩洛哥沿海上升流方面的能力和稳健性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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