A Multiscale Deep Framework for Ocean Fronts Detection and Fine-Grained Location

Abstract

Ocean front plays an important role in marine fishery production and biogeochemical cycling. This letter proposes a multiscale deep framework to meet the need for automatic ocean front detection and fine-grained location. The framework mainly focuses on bringing a well-trained deep learning model into front detection and location on the global satellite sea surface temperature image. First, a multiscale scanner is designed to divide the ocean into small areas of different scales. Then, we introduce the deep model to determine that a front has occurred, and translate the global image into binary ones of various grained. Here, an overlapping scanning way is suggested to locate the front in a small region. Finally, all the binary images are scale-weighted fused into one image, which presents the center and periphery with different brightness levels. Experimental illustrations on three typical areas of the ocean are featured with six scanning scales to show the effectiveness and practical use of the proposed framework. Moreover, the comparison experiments with the traditional method also show its advantages.