Sea ice concentration estimation techniques using machine learning: An end-to-end workflow for estimating concentration maps from SAR images

Stefan Dominicus, Amit Kumar Mishra, Christo Rautenbach

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Sea ice concentration (SIC) is an important metric used to characterize polar sea ice behavior. Understanding this behavior and accurately representing it is of critical importance for climate science research and also has important uses in the context of maritime navigation. An end-to- end workflow for generating learned concentration estimation models from synthetic aperture radar (SAR) data, trained on existing passive microwave (PMW) data, is presented here. A novel objective function was introduced to account for uncertainty in the PMW measurements, which can be extended to account for arbitrary sources of error in the training data, and a recent set of in situ observations was used to evaluate the reliability of the chosen PMW concentration estimation model. Google Colaboratory was used as the development platform, and all notebooks, training data, and trained models are available on GitHub. This chapter is an overview of the most interesting aspects of this investigation, and a detailed report is also available on GitHub.

Original languageEnglish
Title of host publicationNew Methodologies for Understanding Radar Data
PublisherInstitution of Engineering and Technology
Chapter10
Pages319-337
Number of pages19
ISBN (Electronic)9781839531897
ISBN (Print)9781839531880
DOIs
Publication statusPublished - Oct 2021
Externally publishedYes

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