Operational Sea-Ice Classification of Dual Polarized SAR Imagery

• Main Author: Jiang, Mingzhe
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: University of Waterloo 2023
• Subjects: sea ice; synthetic aperture radar (SAR); classification; machine learning; deep learning; Sea ice
• Online Access: http://hdl.handle.net/10012/20123

Mapping sea ice in polar regions is crucial for research and operational applications, such as environmental modeling and ship navigation. Synthetic aperture radar (SAR) offers a dependable and efficient means of monitoring sea ice under various weather conditions and operational scenarios. Presently, national ice services, such as the Canadian Ice Service (CIS), rely on experienced ice analysts to manually interpret SAR imagery and generate ice charts. Although these charts have been in use for decades, they possess several shortcomings. Manual interpretation necessitates significant expert resources and introduces human bias, and the charts only provide a region-based approximation of ice conditions. Consequently, automated sea-ice classification systems are highly desirable, aiming to accurately label each pixel in SAR imagery with the corresponding sea-ice type. Addressing the challenges in sea ice classification, such as intra- and inter-class variance, has proven difficult for single-model-based systems due to the limited spatial and contextual information captured. This thesis introduces sea ice classification methods comprising two primary stages: unsupervised segmentation to generate homogeneous regions, and labeling to assign each homogeneous region an appropriate label. Two innovative methods that directly combine segmentation and labeling for automated sea ice classification are presented. Firstly, a convolutional neural network (CNN) based approach, inspired by the rapid advancements in deep-learning architectures, is developed to differentiate between sea ice and open water. A regional pooling layer is introduced to harness the spatial features learned through labeling and the contextual information extracted via segmentation. Since CNN models necessitate extensive labeled samples, which are scarce for various ice types, a random-forest-based method trained on limited labeled samples is formulated. Texture features are initially extracted from each pixel and then combined with an energy function to ...