Baltic Sea Ice Concentration Estimation Based on C-Band HH-Polarized SAR Data

Ice concentration measurements are important information e.g., for navigation, ice modeling, and climate change research. Here we present an algorithm for estimating ice concentration from C-band SAR data. The resolution of SAR data is significantly higher than the resolution of the current operational ice concentration products based on radiometer data. Our algorithm is based on segment-wise autocorrelation distributions. The algorithm results were compared with two reference data sets: ice concentrations from the Finnish Ice Service (FIS) ice charts, and ice concentrations from the radiometer-based operational ice concentration algorithm of University of Bremen. The new algorithm gives reasonable ice concentration estimates in a high resolution (500 m) for an arbitrary segmentation.

[1]  B. Scheuchl,et al.  Potential of RADARSAT-2 data for operational sea ice monitoring , 2004 .

[2]  Leonhard Held,et al.  Gaussian Markov Random Fields: Theory and Applications , 2005 .

[3]  J. Yackel,et al.  Sea ice type and open water discrimination using dual co-polarized C-band SAR , 2009 .

[4]  Mohammed Shokr,et al.  Microwave Emission Observations from Artificial Thin Sea Ice: The Ice-Tank Experiment , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Josefino C. Comiso,et al.  Characteristics of Arctic winter sea ice from satellite multispectral microwave observations , 1986 .

[6]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[7]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[8]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[9]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[10]  David A. Clausi,et al.  Designing Gabor filters for optimal texture separability , 2000, Pattern Recognit..

[11]  David A. Clausi,et al.  Gaussian MRF rotation-invariant features for image classification , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[13]  Marko Mäkynen,et al.  Open water detection from Baltic Sea ice Radarsat-1 SAR imagery , 2005, IEEE Geoscience and Remote Sensing Letters.

[14]  W. Campbell,et al.  Determination of sea ice parameters with the NIMBUS 7 SMMR , 1984 .

[15]  Anders Berg Spaceborne SAR in Sea Ice Monitoring: Algorithm Development and Validation for the Baltic Sea , 2011 .

[16]  Martti Hallikainen,et al.  Incidence angle dependence of the statistical properties of C-band HH-polarization backscattering signatures of the Baltic Sea ice , 2002, IEEE Trans. Geosci. Remote. Sens..

[17]  Bedrich J. Hosticka,et al.  A comparison of texture feature extraction using adaptive gabor filtering, pyramidal and tree structured wavelet transforms , 1996, Pattern Recognit..

[18]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[19]  David A. Clausi,et al.  Comparison and fusion of co‐occurrence, Gabor and MRF texture features for classification of SAR sea‐ice imagery , 2001 .

[20]  Ola M. Johannessen,et al.  Sea Ice Monitoring by Remote Sensing , 2006 .

[21]  David A. Clausi,et al.  Operational map-guided classification of SAR sea ice imagery , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[22]  David A. Clausi,et al.  SAR Sea-Ice Image Analysis Based on Iterative Region Growing Using Semantics , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Bin Cheng,et al.  Modelling of ice thermodynamics in natural water bodies , 1998 .

[24]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[25]  David A. Clausi,et al.  Unsupervised segmentation of synthetic aperture Radar sea ice imagery using a novel Markov random field model , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[26]  L. Kaleschke,et al.  Sea ice remote sensing using AMSR‐E 89‐GHz channels , 2008 .