Remote sensing of sea ice leads with Sentinel-1 C-band synthetic aperture radar ...

• Main Author: Murashkin, Dmitrii
• Format: Thesis
• Language: English
• Published: Universität Bremen 2024
• Subjects: remote sensing; sea ice; leads; synthetic aperture radar; SAR; machine learning; deep learning; GLCM; CNN; Arctic; 550; Arctic Ocean; Sea ice
• Online Access: https://dx.doi.org/10.26092/elib/3049; https://media.suub.uni-bremen.de/handle/elib/8015

The presence of leads with open water or thin ice is an important feature of the Arctic sea ice cover. Leads regulate the heat, gas, and moisture fluxes between the ocean and atmosphere and are areas of high ice growth rates during periods of freezing conditions. In the present study an algorithm providing an automatic lead detection based on Synthetic Aperture Radar (SAR) images is developed using traditional machine learning techniques and deep learning methods. The algorithm is applied to a wide range of Sentinel-1 scenes taken over the Arctic Ocean. Distribution of the detected leads in the Arctic during winter seasons 2016--2021 is then analyzed. An important part of the algorithm development is the data preprocessing as the classification quality depends on the quality of the input images. An advanced data preparation technique improves consistency of the cross-polarization channel and enables the use of dual-polarization SAR images. By using both the HH and the HV channels instead of single ...