Transferred Deep Learning for Sea Ice Change Detection From Synthetic-Aperture Radar Images

Abstract

High-quality sea ice monitoring is crucial to navigation safety and climate research in the polar regions. In this letter, a transferred multilevel fusion network (MLFN) is proposed for sea ice change detection from synthetic-aperture radar (SAR) images. Considering the fact that training data are limited in the task of sea ice change detection, a large data set was used to train the MLFN, and the deep knowledge can be transferred to sea ice analysis. In addition, cascade dense blocks are employed to optimize the convolutional layers. Multilayer feature fusion is introduced to exploit the complementary information among low-, mid-, and high-level feature representations. Therefore, more discriminative feature extraction can be achieved by the MLFN. Furthermore, the fine-tune strategy is utilized to optimize the network parameters. The experimental results on two real sea ice data sets demonstrated that the proposed method achieved better performance than other competitive methods.