Abstract
It is difficult to balance local details and global distribution using a single source image in marine target detection of a large scene. To solve this problem, a technique based on the fusion of optical image and synthetic aperture radar (SAR) image for the extraction of sea ice is proposed in this paper. The Band 2 (B2) image of Sentinel-2 (S2) in the research area is selected as optical image data. Preprocessing on the optical image, such as resampling, projection transformation and format conversion, are conducted to the S2 dataset before fusion. Imaging characteristics of the sea ice have been analyzed, and a new deep learning (DL) model, OceanTDL5, is built to detect sea ices. The fusion of the Sentinel-1 (S1) and S2 images is realized by solving the optimal pixel values based on deriving Poisson Equation. The experimental results indicate that the use of a fused image improves the accuracy of sea ice detection compared with the use of a single data source. The fused image has richer spatial details and a clearer texture compared with the original optical image, and its material sense and color are more abundant.
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