Compaction of C-band synthetic aperture radar based sea ice information for navigation in the Baltic Sea

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Dissertations in computer and information science. Report D, 17
In this work operational sea ice synthetic aperture radar (SAR) data products were improved and developed. A SAR instrument is transmitting electromagnetic radiation at certain wavelengths and measures the radiation which is scattered back towards the instrument from the target, in our case sea and sea ice. The measured backscattering is converted to an image describing the target area through complex signal processing. The images, however, differ from optical images, i.e. photographs, and their visual interpretation is not straightforward. The main idea in this work has been to deliver the essential SAR-based sea ice information to end-users (typically on ships) in a compact and user-friendly format. The operational systems at Finnish Institute of Marine Research (FIMR) are currently based on the data received from a Canadian SAR-satellite, Radarsat-1. The operational sea ice classification, developed by the author with colleagues, has been further developed. One problem with the SAR data is typically that the backscattering varies depending on the incidence angle. The incidence angle is the angle in which the transmitted electromagnetic wave meets the target surface and it varies within each SAR image and between different SAR images depending on the measuring geometry. To improve this situation, an incidence angle correction algorithm to normalize the backscattering over the SAR incidence angle range for Baltic Sea ice has been developed as part of this work. The algorithm is based on SAR backscattering statistics over the Baltic Sea. To locate different sea ice areas in SAR images, a SAR segmentation algorithm based on pulse-coupled neural networks has been developed and tested. The parameters have been tuned suitable for the operational data in use at FIMR. The sea ice classification is based on this segmentation and the classification is segment-wise rather than pixel-wise. To improve SAR-based distinguishing between sea ice and open water an open water detection algorithm based on segmentation and local autocorrelation has been developed. Also ice type classification based on higher-order statistics and independent component analysis have been studied to get an improved SAR-based ice type classification. A compression algorithm for compressing sea ice SAR data for visual use has been developed. This algorithm is based on the wavelet decomposition, zero-tree structure and arithmetic coding. Also some properties of the human visual system were utilized. This algorithm was developed to produce smaller compressed SAR images, with a reasonable visual quality. The transmission of the compressed images to ships with low-speed data connections in reasonable time is then possible. One of the navigationally most important sea ice parameters is the ice thickness. SAR-based ice thickness estimation has been developed and evaluated as part of this work. This ice thickness estimation method uses the ice thickness history derived from digitized ice charts, made daily at the Finnish Ice Service, as its input, and updates this chart based on the novel SAR data. The result is an ice thickness chart representing the ice situation at the SAR acquisition time in higher resolution than in the manually made ice thickness charts. For the evaluation of the results a helicopter-borne ice thickness measuring instrument, based on electromagnetic induction and laser altimeter, was used.
synthetic aperture radar, SAR, sea ice, classification, pulse-coupled neural network, PCNN, wavelets, image compression, independent component analysis
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  • J. Karvonen, M. Similä, M. Mäkynen, An Iterative Incidence Angle Normalization Algorithm for Sea Ice SAR Images, Proceedings of the 2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002), vol. III, pp. 1524-1528, 2002.
  • J. Karvonen, M. Similä, A Wavelet Transform Coder Supporting Browsing and Transmission of Sea Ice SAR Imagery, IEEE Transactions on Geoscience and Remote Sensing, vol. 40, n. 11, pp. 2464-2485, 2002.
  • J. Karvonen, Baltic Sea Ice SAR Segmentation and Classification Using Modified Pulse-Coupled Neural Networks, IEEE Transactions on Geoscience and Remote Sensing, vol. 42, n. 7, pp. 1566-1574, 2004.
  • J. Karvonen, M. Similä, M. Mäkynen, Open Water Detection from Baltic Sea Ice Radarsat-1 SAR Imagery, IEEE Geoscience and Remote Sensing Letters, vol. 2, n. 3, pp. 275-279, 2005.
  • J. Karvonen, Feature Detection from Preprocessed Sea Ice SAR Data Based on Higher-Order Statistics, Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2003), vol. VI, pp. 3450-3452, 2003.
  • J. Karvonen, M. Similä, Independent Component Analysis for Sea Ice SAR Image Classification, Proceedings of the 2001 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2001), vol. III, pp. 1255-1257, 2001.
  • J. Karvonen, M. Similä, ICA-Based Classification of Sea Ice SAR Images, Proceedings of the 23rd European Association of Remote Sensing Laboratories (EARSeL) Annual Symposium, Gent, Belgium, pp. 211-217, 2003. Millpress 2004.
  • J. Karvonen, M. Similä, I. Heiler, Ice Thickness Estimation Using SAR Data and Ice Thickness History, Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2003), vol. I, pp. 74-76, 2003.
  • J. Karvonen, M. Similä, J. Haapala, C. Haas, M. Mäkynen, Comparison of SAR Data and Operational Sea Ice Products to EM Ice Thickness Measurements in the Baltic Sea, Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2004), vol. V, pp. 3021-3024, 2004.
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