Classification of Synthetic Aperture Radar Images using Fuzzy SVMs

Abstract

In this study, the classification of synthetic aperture radar images are analyses using fuzzy support vector machines. The issues in the classification of SAR images are not addressed properly and remains an unsolved problem in few instances. This study provides one of the best methodology to classify SAR based image databases with fuzzy support vector machines. All the concepts which are derived and applied are based machine learning algorithms. The designed and developed methodology has got many applications in the area like weather forecasting, flood impact monitoring, designing crop calenders, early detection of drought and many more. All the algorithms applied in this study are pixel oriented. This study is carried out on the SAR images collected from the publicly available AXA EORC data set. We designed a model based mathematics and statistics which is developed in the python programming language. Several algorithms are designed to analyze SAR images in which fuzzy support methodology is exhibited high performance. Reports discussed in this study shown that the efficiency of fuzzy support vector machines to classify synthetic aperture radar image is 95.3% without any heuristic information.