Comparing automated sea ice classification on single-pol and dual-pol TerraSAR-X data

We compare classification of sea ice based on TerraSAR-X (TS-X) images for single-polarization and dual-polarization imaging modes. A texture based implementation for neural network classification on single-polarized ScanSAR data is presented. Likewise we propose an approach for operational generation of dual-polarized Stripmap data (with a different neural network architecture). Polarimetric feature quality in terms of information content is discussed for the latter implementation. Based on these results, neural network classification is applied to image acquired over Svalbard, Baffin Bay, and the Barents Sea. Our successful results justify to increase efforts into exploring further application potential of a software suite which comprises both algorithms. Such a tool may then provide navigational assistance for maritime users in near-real time.

[1]  Torbjørn Eltoft,et al.  Characterization of Marine Surface Slicks by Radarsat-2 Multipolarization Features , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Jane Labadin,et al.  Feature selection based on mutual information , 2015, 2015 9th International Conference on IT in Asia (CITA).

[4]  Eric Pottier,et al.  An entropy based classification scheme for land applications of polarimetric SAR , 1997, IEEE Trans. Geosci. Remote. Sens..

[5]  Ola M. Johannessen,et al.  Classification of Sea Ice Types in ENVISAT Synthetic Aperture Radar Images , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Jaan Praks,et al.  Alternatives to Target Entropy and Alpha Angle in SAR Polarimetry , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Susanne Lehner,et al.  Texture-based sea ice classification on TerraSAR-X imagery , 2014 .

[8]  Huan Liu,et al.  Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution , 2003, ICML.

[9]  Ola M. Johannessen,et al.  Multisensor approach to automated classification of sea ice image data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

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

[11]  Susanne Lehner,et al.  A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  Leen-Kiat Soh,et al.  Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices , 1999, IEEE Trans. Geosci. Remote. Sens..