SVM-Based Classification for Identification of Ice Types in SAR Images Using Color Perception Phenomena
In rise of global temperatures, the formation of ice in freshwater like rivers and lakes are apparent to high condition which has to be significantly monitored for the importance of forecasting and hydropower generation. For this research, Synthetic Aperture Radar (SAR) based images gives good support in mapping the variation between the remote sensing data analysis. This paper presents an approach to map the different target signatures available in the radar image using support vector machine by providing limited amount of reference data. The proposed methodology takes a preprocess expansion of transforming the grayscale image into a synthetic color image which is often used with radar data to improve the display of subtle large-scale features. Hue Saturation Value based sharpened Synthetic Aperture Radar images are used as the input to supervised classifier in which evaluation metrics are considered to assess both the phase of the approach. Based on the evaluation, Support Vector Machine classifier with linear kernel has been known to strike the right balance between accuracy obtained on a given finite amount of training patterns and the facility to generalize to undetected data.
KeywordsSAR images sharpening techniques supervised classification SVM kernel κ-coefficient
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