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Article summary:
1. This paper presents an approach to sea ice classification using dual polarization RADARSAT-2 ScanSAR data based on support vector machine (SVM).
2. The method uses backscatter coefficients, gray-level cooccurrence matrix (GLCM) texture features, and sea ice concentration as a basis for classification.
3. The results showed that the sea ice concentration parameter was effective in dealing with open water and in discriminating pancake ice from old ice.
Article analysis:
The article is generally reliable and trustworthy, providing a detailed overview of the research conducted on sea ice classification using dual polarization RADARSAT-2 ScanSAR data. The authors provide evidence for their claims by citing relevant studies and experiments conducted by other researchers in the field. Furthermore, they present both sides of the argument equally, noting potential risks associated with their proposed method as well as possible improvements that could be made to it. Additionally, the authors provide a comprehensive list of references at the end of the article which further adds to its credibility.
However, there are some areas where the article could be improved upon. For example, while the authors discuss potential risks associated with their proposed method, they do not provide any evidence or examples to illustrate these risks in detail. Additionally, while they mention other methods such as neural networks and wavelets which have been used for sea ice classification in previous studies, they do not explore these methods in depth or compare them to their own proposed method. Finally, while they cite several studies throughout the article to support their claims, some of these studies are quite old and may no longer be relevant given recent advances in technology and methodology.