Multifeature fusion for polarimetric synthetic aperture radar image classification of sea ice

Abstract Sea ice conditions are so heterogeneous, and the differences between the different ice types are less varied than that of land targets, so only using polarimetric or textural features would lead to misclassification of polarimetric synthetic aperture radar (PolSAR) data of sea ice. To support the identification of different ice types, the fusion of textural and polarimetric features would be a good solution. Simple discrimination analysis is used to rationalize a preferred features subset. Some features are analyzed, which include entropy H / alpha α / anisotropy A and three kinds of texture statistics (entropy, contrast, and correlation), in the C- and L-band polarimetric mode. After that, a multiobjective fuzzy decision model is proposed for supervised PolSAR data classification of sea ice, and the targets are categorized according to the principle of maximum membership grade. In consideration of the interference of the correlation among features, the model is based on Mahalanobis distance in which the covariances between the selected heterogeneous features could restrain the interference among redundant features. In the end, the effectiveness of the algorithm for PolSAR image classification of sea ice is demonstrated through the analysis of some experimental results.

[1]  B. Scheuchl,et al.  Automated sea ice classification using spaceborne polarimetric SAR data , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[2]  Mitchell R. Grunes,et al.  Polarimetic SAR Speckle Filtering and Terrain Classification an Overview , 1999 .

[3]  Eric Rignot,et al.  Identification of sea ice types in spaceborne synthetic aperture radar data , 1992 .

[4]  Jong-Sen Lee,et al.  The use of fully polarimetric information for the fuzzy neural classification of SAR images , 2003, IEEE Trans. Geosci. Remote. Sens..

[5]  Juha A. Karvonen,et al.  Baltic Sea ice SAR segmentation and classification using modified pulse-coupled neural networks , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[6]  John J. Yackel,et al.  Evaluation of C-band SAR polarimetric parameters for discrimination of first-year sea ice types , 2012 .

[7]  David A. Clausi,et al.  Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Eric Rignot,et al.  Multifrequency Polarimetric Synthetic Aperture Radar Observations of Sea Ice , 1991 .

[9]  D. G. Barber,et al.  Multivariate Analysis of Texture Statistics for SAR Sea Ice Discrimination , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[10]  E. Pottier,et al.  Unsupervised Wishart Classifications of Sea-Ice using Entropy, Alpha and Anisotropy decompositions , 2003 .

[11]  David A. Clausi,et al.  IRGS: Image Segmentation Using Edge Penalties and Region Growing , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[13]  David A Clausi An analysis of co-occurrence texture statistics as a function of grey level quantization , 2002 .

[14]  S. K. Sengupta,et al.  Cloud field classification based upon high spatial resolution textural features: 2. Simplified vector approaches , 1989 .

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

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

[17]  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.

[18]  Jong-Sen Lee,et al.  Fuzzy classification of earth terrain covers using complex polarimetric SAR data , 1996 .

[19]  Leen-Kiat Soh,et al.  Unsupervised segmentation of ERS and Radarsat sea ice images using multiresolution peak detection and aggregated population equalization , 1999 .

[20]  Mohammed Dabboor,et al.  New segmentation algorithms for dual and full polarimetric SAR data , 2011 .

[21]  B. Holt,et al.  SIR-C polarimetric radar results from the Weddell Sea, Antarctica , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[22]  Rob J. Dekker,et al.  Texture analysis and classification of ERS SAR images for map updating of urban areas in The Netherlands , 2003, IEEE Trans. Geosci. Remote. Sens..

[23]  Torbjørn Eltoft,et al.  Comparison of automatic segmentation of full polarimetric SAR sea ice images with manually drawn ice charts , 2013 .

[24]  Bell Telephone,et al.  ROBUST ESTIMATES, RESIDUALS, AND OUTLIER DETECTION WITH MULTIRESPONSE DATA , 1972 .

[25]  David G. Barber,et al.  On the relationship between spatial patterns of sea‐ice type and the mechanisms which create and maintain the North Water (NOW) polynya , 2001 .