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Unsupervised detection and quantification of iceberg populations within sea ice from dual-polarisation SAR imagery

Citation

Evans, B and Fleming, A and Faul, A and Hosking, S and Lieser, J and Fox, M, Unsupervised detection and quantification of iceberg populations within sea ice from dual-polarisation SAR imagery, EGU General Assembly 2022 Book of Abstracts, 23-27 May 2022, Vienna, Austria & Online, pp. EGU22-8267. (2022) [Conference Extract]


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Official URL: https://meetingorganizer.copernicus.org/EGU22/EGU2...

DOI: doi:10.5194/egusphere-egu22-8267

Abstract

Accurate estimates of iceberg populations, disintegration rates and iceberg movements are essential to fully understand ice sheet contributions to sea level rise and freshwater and heat balances. Understanding and prediction of iceberg distributions is also of paramount importance for the safety of commercial and research shipping operations in polar seas. Despite their manifold implications the operational monitoring of icebergs remains challenging, largely due to difficulties in automating their detection at scale.

Synthetic Aperture Radar (SAR) data from satellites, by virtue of its ability to penetrate cloud cover and strong sensitivity to the dielectric properties of the reflecting surface, has long been recognised as providing great potential for the identification of icebergs. Many existing studies have developed algorithms to exploit this data source but the majority are designed for open water situations, require significant operator input, and are susceptible to the substantial spatial and temporal variability in backscatter characteristics within and between SAR scenes that result from meteorological, geometric and instrumental differences. Further ambiguity arises when detecting icebergs in dense fields close to the calving front and in the presence of sea ice. For detection to be fully automated, therefore, adaptive iceberg detection algorithms are required, of which few currently exist.

Here we propose an unsupervised classification procedure based on a recursive implementation of a Dirichlet Process Mixture Model that is robust to inter-scene variability and is capable of identifying icebergs even within complex environments containing mixtures of open water, sea ice and icebergs of various sizess. The method exploits freely available dual-polarisation Sentinel 1 EW imagery, allowing for wide spatial coverage at a high temporal density and providing scope for near-real-time monitoring. It overcomes many of the limitations of existing approaches in terms of environments to which it may be applied as well as requirements for labelled training datasets or determination of scene-specific thresholds. Thus it provides an excellent basis for operational monitoring and tracking of iceberg populations at a continental scale to inform both scientific and navigational priorities.

Item Details

Item Type:Conference Extract
Keywords:Antarctica, Southern Ocean, icebergs, sea ice, SAR, RADAR
Research Division:Earth Sciences
Research Group:Physical geography and environmental geoscience
Research Field:Glaciology
Objective Division:Environmental Management
Objective Group:Management of Antarctic and Southern Ocean environments
Objective Field:Antarctic and Southern Ocean ice dynamics
UTAS Author:Lieser, J (Dr Jan Lieser)
ID Code:149344
Year Published:2022
Deposited By:Directorate
Deposited On:2022-03-26
Last Modified:2022-03-28
Downloads:0

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