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

IEEE Geoscience and Remote Sensing Letters(2019)

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摘要
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...
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关键词
Feature extraction,Sea ice,Task analysis,Radar polarimetry,Training,Deep learning
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