Identifying Oceanic Eddy With an Edge-Enhanced Multiscale Convolutional Network

Abstract

Identifying oceanic eddy from remotely sensed sea surface height (SSH) data is challenging, mainly because of its large-size variations. This article proposes an automatic identification model upon convolutional neural networks for dealing with this issue. The proposed network is comprised of two branches: an eddy identification branch and an edge extraction branch. Both of them adopt encoder–decoder frameworks, and the encoder is shared with each other. The eddy identification branch simultaneously uses multiscale convolution modules in the encoder and skip-layer connections between the encoder and the decoder to learn multiscale features, thus effectively identifying eddies with different sizes. Differently, the edge extraction branch is designed to learn the edge information of eddies, which is not fully captured by the eddy identification branch. To sufficiently evaluate the identification performance of our proposed model, several experiments are conducted on a public eddy identification dataset named SCSE-Eddy, and the results indicate that the proposed model is capable of achieving higher performance than those of the existing identification models.