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

نویسندگان

چکیده

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 detection. However, performance is unsatisfactory low clouds are hard distinguish on satellite images they similar spectral radiance characteristics. To address this difficulty, new method based two-stage deep learning strategy was proposed detect daytime Yellow Bohai Sea. We first utilized fully connected network separate clear sky from clouds. Then, convolutional neural used extract differences between 16 Advanced Himawari Imager (AHI) bands. In addition, we built Fog (YBSF) dataset by pixel-wise labelling AHI into three categories (i.e., sky, cloud, fog). Five comparable methods were YBSF appraise our method. The vertical feature mask (VFM) generated Cloud-Aerosol Lidar Orthogonal Polarization (CALIOP) also verify accuracy. experimental results demonstrate effectiveness for

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2022

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs14215570