Sea-Ice Image Classification for Channel Navigation in Polar Application

In the paper, we proposed a novel framework for the sea-ice images classification, which will be very helpful for the future channel navigation in polar applications. Firstly, the three different types of sea ice are defined in the paper based on the large amounts of data inspection, such as thick ice, thin ice and water. Further, we present to use relative radiometric normalization method to solve the grayscale difference of different temporal sea-ice images, which can affect the classification accuracy of sea ice. Finally, the SAE (sparse auto-encoder) classifier is employed for different types of sea-ice classification. We designed several experiments to discuss and analyze the influence factors on the classification accuracy of sea-ice images. The experiment results indicated the validity of the proposed method and the rationality of sea ice type definition.