Daytime Sea Fog Detection Based on a Two-Stage Neural Network

Abstract

Sea fog detection has received widespread attention because it plays a vital role in maritime activities. Due to the lack of sea observation data, meteorological satellites with high temporal and spatial resolution have become an essential means of sea fog detection. However, the performance is unsatisfactory because low clouds and sea fog are hard to distinguish on satellite images because they have similar spectral radiance characteristics. To address this difficulty, a new method based on a two-stage deep learning strategy was proposed to detect daytime sea fog in the Yellow Sea and Bohai Sea. We first utilized a fully connected network to separate the clear sky from sea fog and clouds. Then, a convolutional neural network was used to extract the differences between low clouds and sea fog on 16 Advanced Himawari Imager (AHI) observation bands. In addition, we built a Yellow and Bohai Sea Fog (YBSF) dataset by pixel-wise labelling AHI images into three categories (i.e., clear sky, cloud, and sea fog). Five comparable methods were used on the YBSF dataset to appraise the performance of our method. The vertical feature mask (VFM) generated by Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) was also used to verify the detection accuracy. The experimental results demonstrate the effectiveness of the proposed method for sea fog detection.

Keywords

• sea fog detection
• satellite image
• fully connected network (FCN)
• convolutional neural networks (CNN)
• Advanced Himawari Imager (AHI)