Paper Info
Title
SVM-Based Sea Ice Classification Using Textural Features and Concentration From RADARSAT-2 Dual-Pol 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.
Year
DOI
Venue
2015
10.1109/JSTARS.2014.2365215
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
Keywords
Field
DocType
decision trees,geophysical image processing,image classification,image texture,maximum likelihood estimation,oceanographic techniques,radar imaging,radar polarimetry,remote sensing by radar,sea ice,support vector machines,synthetic aperture radar,scansar image quality,backscatter coefficients,backscatter information,classification basis,decision tree,dual polarization radarsat-2 scansar data,gray-level cooccurrence matrix texture characteristics,gray-level cooccurrence matrix texture features,incidence angle normalization,maximum likelihood,noise floor stripe reduction,open water,optimal support vector machine model,pancake ice,sea ice concentration parameter,sea ice types,support vector machine-based sea ice classification,classification,concentration,support vector machine (svm),synthetic aperture radar (sar),noise,nickel,backscatter
Pancake ice,Computer vision,Decision tree,Sea ice concentration,Sea ice,Normalization (statistics),Synthetic aperture radar,Support vector machine,Remote sensing,Backscatter,Artificial intelligence,Mathematics
Journal
Volume
Issue
ISSN
8
4
1939-1404
Citations 
PageRank 
References 
0
0.34
24
Authors
3
Name
Order
Citations
PageRank
Huiying Liu102.03
Huadong Guo245984.66
Lu Zhang334.09