SVM-Based Sea Ice Classification Using Textural Features and Concentration From RADARSAT-2 Dual-Pol ScanSAR Data

TLDR: The sea ice concentration parameter could play a role in SVM classification, and the whole process provided an effective way to classify sea ice using dual polarization ScanSAR data.

Abstract: An approach to sea ice classification using dual polarization RADARSAT-2 ScanSAR data is presented in this paper. It is based on support vector machine (SVM). In addition to backscatter coefficients and gray-level cooccurrence matrix (GLCM) texture features, sea ice concentration was introduced as a classification basis. To better analyze the backscatter information of sea ice types, we considered two steps that could improve the ScanSAR image quality, the noise floor stripe reduction and the incidence angle normalization. Then, effective GLCM texture characteristics from both polarizations were selected using the proper parameters. The third type of information, sea ice concentration, was extracted from the initial SVM classification result after the optimal SVM model was achieved from the training. The final result was generated by implementing the SVM twice and the decision tree once. Using this method, the classification was improved in two aspects, both of which were related to sea ice concentration. The results showed that the sea ice concentration parameter was effective in dealing with open water and in discriminating pancake ice from old ice. Finally, the maximum likelihood (ML) was run as a comparative test. In conclusion, the sea ice concentration parameter could play a role in SVM classification, and the whole process provided an effective way to classify sea ice using dual polarization ScanSAR data.