The importance of incidence angle for GLCM texture features and ancillary data sources for automatic sea ice mapping.

Abstract

Sea ice is a critical component of Earth’s polar regions. Monitoring it is vital for navigation and construction in the Arctic and crucial to understand and mitigate the impacts of climate change. Synthetic aperture radar (SAR) imagery, particularly dual polarized SAR, is commonly used for this purpose due to its ability to penetrate clouds and provide data in nearly all weather conditions. However, relying solely on HH and HV polarizations for automated sea ice mapping models has limitations, as different ice types and conditions may yield similar backscatter signatures. To enhance the accuracy of these classification models, researchers have explored the integration of additional features, including hand-crafted texture features, learned features, and supplementary data sources.

This thesis makes two main contributions to the field of automated sea ice mapping. The first contribution investigates the dependence of incidence angle (IA) on gray level co-occurrence matrix texture features (GLCM) and its impact on sea ice classification. The methodology involved extracting GLCM features from SAR images in dB units and analyzing their dependence on IA using linear regression and class separability metrics. In addition, a Bayesian classifier was trained to compare the classification performance with and without incorporating the IA dependence. The results indicated that the IA effect had a minor impact on classification performance (≈ 1%), with linear regression results indicating that the IA dependence accounts for approximately less 10% of the variance in most cases.

The second contribution evaluates the importance of various data inputs for automated sea ice mapping using the AI4Arctic dataset. A U-Net based model was trained with SAR imagery, passive microwave data from AMSR2, weather data from ERA5, and ancillary data. Ablation studies and the addition of individual data inputs were conducted to assess their impact on model performance. The results demonstrated that including AMSR2, time, and location data significantly increased model performance, especially for the classification accuracy of major ice types in stage of development (SOD). ERA5 data had mixed effects, as it was found not to increase performance when AMSR2 was already included.

These findings are critical for the development of more accurate and efficient automated sea ice mapping systems. The minimal impact of IA dependence on GLCM features suggests that accounting for IA may not be necessary, simplifying the feature extraction process. Identifying the most valuable data inputs allows for the optimization of model performance, ensuring better resource allocation and enhanced operational capabilities in sea ice monitoring. This research provides a foundation for future studies and developments in automated sea ice mapping, contributing to more effective climate monitoring and maritime navigation safety.