论文标题
在地静止海洋颜色成像仪中的海雾检测的双分支神经网络
Dual Branch Neural Network for Sea Fog Detection in Geostationary Ocean Color Imager
论文作者
论文摘要
海雾极大地威胁着海上活动的安全。本文开发了海雾数据集（SFDD）和双分支海雾检测网络（DB-SFNET）。从2010年到2020年，我们研究了黄海和Bohai Sea（118.1°E-E-128.1°E，29.5°N-43.8°N）的所有观察到的海雾事件，并收集每个事件的海雾图像，并从Geostationary Ocean Color Imaly Imager（GOCI）收集数据雾图像。 SFDD中每个图像中海雾的位置被准确标记。所提出的数据集的特征是长期跨度，大量样品和准确的标记，可以显着改善各种海雾检测模型的鲁棒性。此外，本文提出了一个双分支海雾检测网络，以实现准确而整体的海雾检测。 Proporsed DB-SFNET由知识提取模块和双分支可选编码解码模块组成。这两个模块共同提取视觉和统计域的歧视特征。实验显示出有希望的海雾检测结果，F1得分为0.77，关键成功指数为0.63。与现有的先进深度学习网络相比，DB-SFNET在检测性能和稳定性方面表现出色，尤其是在混合云区域。
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°E-128.1°E, 29.5°N-43.8°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.
