Intelligent image interpreter: A semi-automatic detection of ships by image analysis of space-borne SAR image using SVM

Abstract

The objective is to study the usability of microwave remote sensing in the detection of ships and evaluate the potential of SVM in improving the semi-automatic detection accuracy of ships. The research limits use of SAR-Synthetic Aperture Radar (TerraSAR-X High-Resolution Spotlight imagery), ERDAS Imagine, and MATLAB for analysis. EO image interpretation done manually is accurate but is limited by processing cost and time and adverse weather conditions like fog or clouding. While Microwave SAR remote sensing offers cost-effectiveness with better efficiency and flexibility for the identification of ship under all weather conditions. Large amounts of image data generated by SAR systems can quickly overburden a human observer. The paper discusses a robust method of image analysis for visualization and classification of image using SVM (support vector machines) to assess data toward detection of ships and ascertain the accuracy of feature detection in proposed method.