Dual Branch Neural Network for Sea Fog Detection in Geostationary Ocean Color Imager

TL;DR

This paper introduces a new dataset and a dual branch neural network for accurate sea fog detection using geostationary satellite imagery, significantly improving detection robustness and performance.

Contribution

The paper presents a comprehensive sea fog dataset (SFDD) and a novel dual branch detection network (DB-SFNet) that jointly leverages visual and statistical features for enhanced accuracy.

Findings

Achieved an F1-score of 0.77 in sea fog detection.

DB-SFNet outperforms existing models in detection accuracy and stability.

Effective in mixed cloud and fog conditions.

Abstract

Sea fog significantly threatens the safety of maritime activities. This paper develops a sea fog dataset (SFDD) and a dual branch sea fog detection network (DB-SFNet). We investigate all the observed sea fog events in the Yellow Sea and the Bohai Sea (118.1{\deg}E-128.1{\deg}E, 29.5{\deg}N-43.8{\deg}N) from 2010 to 2020, and collect the sea fog images for each event from the Geostationary Ocean Color Imager (GOCI) to comprise the dataset SFDD. The location of the sea fog in each image in SFDD is accurately marked. The proposed dataset is characterized by a long-time span, large number of samples, and accurate labeling, that can substantially improve the robustness of various sea fog detection models. Furthermore, this paper proposes a dual branch sea fog detection network to achieve accurate and holistic sea fog detection. The poporsed DB-SFNet is composed of a knowledge extraction module and a dual branch optional encoding decoding module. The two modules jointly extracts discriminative features from both visual and statistical domain. Experiments show promising sea fog detection results with an F1-score of 0.77 and a critical success index of 0.63. Compared with existing advanced deep learning networks, DB-SFNet is superior in detection performance and stability, particularly in the mixed cloud and fog areas.