Sea ice classification from RADARSAT constellation mission images using normalizer-free ResNet

• Main Author: Lyu, Hangyu
• Format: Thesis
• Language: English
• Published: Memorial University of Newfoundland 2022
• Subjects: The Sentinel; Sea ice
• Online Access: https://research.library.mun.ca/15658/; https://research.library.mun.ca/15658/1/thesis.pdf

Sea ice monitoring plays a vital role in climate study, maritime navigation and offshore industries. Sea ice monitoring consists of different applications, such as ice classification, concentration and thickness retrieval. As one of the branches of sea ice monitoring, sea ice classification is an essential task in sea ice mapping and the premise to obtain other sea ice parameters. Satellite images are the primary source for sea ice classification due to the broad coverage, the extremely harsh environment in the polar regions and the near real-time requirements of some applications. Spaceborne Synthetic Aperture Radar (SAR) has been widely used as an effective tool for sea ice sensing for decades because it can collect data day and night and in all weather conditions. As a typical representative of the next generation SAR mission, the RADARSAT Constellation Mission (RCM) provides three C-band SAR satellites with shorter revisit time and broader spatial coverage, which will be widely used in various earth observation applications including sea ice sensing. The Sentinel-1 mission comprises two C-band SAR satellites with dual-polarized imaging capability, providing open and free data from the European Space Agency (ESA). Both RCM and Sentinel-1 C-band SARs operate at a center frequency of 5.405 GHz. In addition, RCM provides more spatial coverage and a shorter revisit time than Sentinel-1. However, actual RCM data have not been used for sea ice classification, and no study for comparing the sea ice classification performances of RCM and Sentinel-1 has been conducted. Deep convolutional neural networks (CNN) have been extensively employed in sea ice monitoring applications in the last decade. An example of deep CNN, Normalizer-Free ResNet (NFNet) was proposed by DeepMind in early 2021 and achieved a new state-of-the-art accuracy on the ImageNet dataset. In this thesis, a NFNet based approach has been proposed for sea ice classification using dual-polarized SAR data. In the first part of this study, the RCM data are ...