Robust Multi-Seasonal Ice Classification from High Resolution X-Band SAR

Automated solutions for sea ice type classification from synthetic aperture (SAR) imagery offer an opportunity to monitor sea ice, unimpeded by cloud cover or the arctic night. However, there is a common struggle to obtain accurate classifications year round; particularly in the melt and freeze-up seasons. During these seasons, the radar backscatter signal is affected by wet snow cover, obscuring information about underlying ice types. By using additional spatiotemporal contextual data and a combination of convolutional neural networks and a dense conditional random field, we can mitigate these problems and obtain a single classifier which is able to classify accurately at 3.5 m spatial resolution for five different classes of sea ice surface from October to May. During the near year-long drift of the MOSAiC expedition we collected satellite scenes of the same patch of Arctic pack ice with X-Band SAR with a revisit-time of less than a day on average. Combined with in-situ observations of the local ice properties this offers up the unprecedented opportunity to perform a detailed and quantitative assessment of the robustness of our classifier for level, deformed and heavily deformed ice. For these three classes, we can perform accurate classification with a probability > 95% and calculate a lower bound for the robustness between 85% and 88%

Mit Radarsatelliten durch die Polarnacht

Der Klimawandel und die damit verbundene Erderwärmung ergreifen unseren Planeten und verwandeln Ökosysteme und Biotope in einem rasanten Tempo. Die Arktis, wo dieser Wandel am stärksten ist, taucht die Hälfte des Jahres in die Dunkelheit der Polarnacht – was die Beobachtung der einzigartigen, nördlichen Eiswüsten erschwert. Dieser Vortrag zeigt, wie ein Satellit mit einem Radar die Dunkelheit der Polarnacht überwinden kann und einen Einblick in die verschwindenden Welten des arktischen Meereises liefert

Realising the potential of data driven sea ice retrieval methods from SAR

The remoteness and environmental hostility of the Arctic and Antarctic regions greatly impact polar remote sensing research, because high-resolution ground measurements are sparse and have only limited tempo-spatial validity. In the case of sea ice class retrieval from space-borne synthetic aperture radar (SAR), research thus becomes heavily reliant on human annotated datasets. Due to the limited time that a human observer can spend on a scene and the difficulty of labelling sea ice from the backscatter alone, these annotations suffer from a range of drawbacks. Real (measured) ground truth data will likely not become readily available for a large range of SAR acquisitions at high resolution and coverage. Thus, it is difficult to realize the potential of data driven algorithms: To become increasingly more proficient with the influx of more reference data. The only way to build such retrieval algorithms is to be independent of additional data sources which are not readily available. This implies that (high-resolution) ice classification is not a task that can reap the benefits of data-driven algorithms, as added data in the form of high-resolution labels is required but not available. However, we can use local incidence angle dependence of sea ice backscatter as a proxy for ice class labels: Using physics informed networks enables learning such incidence angle dependencies without any additional data but the SAR imagery. This allows for a sustainable sea ice retrieval method, that circumvents a majority of shortcomings originating from the lack of readily available ground truth and is truly able to improve with the SAR data alone

Data preparation: 1. Introduction and image data

Data preparation Data holds the answers to all manner of questions and analysis methods can extract those answers - linking the two is “data preparation”. Anyone interested in working with data will likely need to know at least some of the principles outlined. Furthermore, cross-discipline data analysis is part of the scientific progress. This workshop offers the opportunity to look into data preparation procedures for different data types and to learn about their individual characteristics: 1. Introduction and image data An introduction to the preparation of data for analysis, beginning with the initial production or acquisition through to an analysis ready dataset. The specific case of image data will then be discussed in more detail using examples from satellite-based Earth Observation