文章基本信息

标题：SAR Ice Image Classification Using Parallelepiped Classifier Based on Gram-Schmidt Spectral Technique
本地全文：下载
作者：A.Vanitha ; P.Subashini ; M.Krishnaveni 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2013
卷号：3
期号：5
页码：385-392
DOI：10.5121/csit.2013.3540
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：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 high-resolution pixel size. Based on the performance evaluation metrics it is proved that Gram-Schmidt 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
关键词：SAR Ice classification; Gram-Schmidt spectral sharpening; supervised classification; ;unsupervised classification; Kappa coefficient