SAR Ice Image Classification Using Parallelepiped Classifier Based on Gram-Schmidt Spectral Technique

Abstract

Synthetic Aperture Radar (SAR) is a special type of imaging radar that involves advanced technology and complex data processing to obtain detailed images from the lake surface. Lake ice typically reflects more of the radar energy emitted by the sensor than the surrounding area, which makes it easy to distinguish between the water and the ice surface. In this research work, SAR images are used for ice classification based on supervised and unsupervised classification algorithms. In the pre-processing stage, Hue saturation value (HSV) and Gram–Schmidt spectral sharpening techniques are applied for sharpening and resampling to attain highresolution pixel size. Based on the performance evaluation metrics it is proved that GramSchmidt spectral sharpening performs better than sharpening the HSV between the boundaries. In classification stage, Gram–Schmidt spectral technique based sharpened SAR images are used as the input for classifying using parallelepiped and ISO data classifier. The performances of the classifiers are evaluated with overall accuracy and kappa coefficient. From the experimental results, ice from water is classified more accurately in the parallelepiped supervised classification algorithm.