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Abstract

This study constructs four deep-learning OceanTDLx series models and uses a WinR-AdaGrad gradient descent algorithm to train and optimize the constructed models. Through an analysis of the loss, accuracy, and time consumption of the four models (i.e., OceanTDL2, OceanTDL3, OceanTDL5 and OceanTDL8), we reveal that the models’ performance does not improve when the number of layers is increased and that OceanTDL5 provides the optimal performance. OceanTDL5 is compared with OceanTDA9 (a model that we previously constructed), and the curves for training loss_batch and training accuracy_batch indicate that OceanTDL5 is more suitable than OceanTDA9 for detecting distributed targets, particularly semi-melted sea ice, which is intertwined and easily confused with seawater. We process the SAR (Synthetic Aperture Radar) data of the research area and obtain a data set with a 10-m resolution, which is then used to verify the effectiveness of the constructed models for sea ice detection. The results reveal that OceanTDL5 has a detection capacity of approximately 55.6 km2 /s and a detection accuracy rate of 97.5%. Compared with traditional ocean target detection methods, OceanTDL5 has greater detection speed and accuracy.

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