A Deep Learning Model for Oceanic Mesoscale Eddy Detection Based on Multi-source Remote Sensing Imagery
Yingjie Liu, Xiaofeng Li, Yibin Ren
Abstract
Mesoscale eddies are circular flowing currents that can retain and transport salt, heat, and nutrients all around the ocean. Mesoscale eddies can be detected on remote sensing images, i.e., sea surface height (SSH) images, sea surface temperature (SST) images, chlorophyll concentration images, etc. Most existing automatic eddy detection algorithms are developed based on one kind of remote sensing data. There is a lack of an automatic eddy detection algorithm that can make full use of multi-source remote sensing data to ensure the accuracy and efficiency of eddy detection. The paper proposes a multi-modal U-Net model, a deep neural network-based framework for eddy detection from multi-source remote sensing images. Compared with the previous eddy detection methods, the newly proposed method improves the accuracy and efficiency of eddy detection by using fusion data of SSH and SST.