CIRFA welcomes you to a seminar on the topics of iceberg detection and sea ice classification.
Date: Thursday 31 August, 14h30-15h30
Venue: CIRFA, SIVA Innovation Centre (Forskningsparken), Tromsø [map]
Presentations by Vahid Akbari and Cornelius Quigley.
Vahid Akbari, Postdoctoral researcher, UiT the Arctic University of Norway
Icebergs can cause a significant threat to shipping, offshore oil and gas production facilities, and subsea pipelines. Synthetic aperture radar is a well established tool for detecting and monitoring sea ice objects in the often dark and cloud covered polar regions. However, detection of small icebergs floating in non-homegeous sea clutter environments is still a challenging task. We propose a new methodology for automatic identification of potential icebergs in high-resolution polarimetric synthetic aperture radar images. The algorithm adopts to various sea-ice conditions and it tackles high iceberg density situations and heterogeneous background conditions in the marginal ice zone. Results from a time-series of RADARSAT-2 data containing numerous icebergs broken off from glaciers in Kongsfjorden on Svalbard demonstrates that the approach is viable.
A comparison between optical and SAR classification results for thin sea ice in Storfjorden
Cornelius Quigley, PhD fellow, CIRFA/UiT the Arctic University of Norway
According to the scientific consensus, the Arctic is currently in a state of unprecedented change. In recent years, climate change has been identified as the main cause of Arctic sea ice decline. For this reason, the need to have access to timely and cost effective data from Earth orbiting satellites is of great importance. This study is concerned with classifying thin sea ice in Storfjorden using data acquired from both MODIS and Radarsat-2 in order to determine if data from either sources can be considered complimentary to each other. For this purpose, four comparisons were made. These included comparing MODISs 36 band data set with data from Radarsat-2 ScanSAR Narrow and Wide modes. As well as this, a comparison between MODISs 36 band data and data from Radarsat-2 QuadPol mode was made. HEM thickness measurements are also available from a helicopter campaign around the same time the data was taken. From laser altimeter data that accompanied the thickness measurements, a roughness characteristic was derived that was compared against the HEM measured thicknesses. The features that were chosen are a set of six basic features that have shown reasonably good results in the segmentation of sea ice previously and are known as the Extended Polarimetric Feature Space (EPFS). This set of features is composed of five polarimetric features plus a feature for non-Gaussianity. All features were segmented using a Mixture of Gaussian algorithm with Markov Random Field based contextual smoothing. The segmented results were compared visually by using all a priori knowledge about the fjord sourced from weather charts and scientific papers. The best results were found for the comparison between MODIS data and Radarsat-2 ScanSAR Wide data. This comparison shows that for a low number of clusters, the segmentation algorithm finds the same surface classes in the MODIS data as it does for the Radarsat-2 data. However, for progressively higher number of clusters of the MODIS data, MODIS reveals information related to the large-scale ice types present in the fjord that the SAR is insensitive to.
Welcome to all!