SAR Image Understanding using Contextual Information

摘要

A fundamental pre-cursor to synthetic aperture radar (SAR) interpretation is the segmentation of the image into statistically homogeneous regions for which very reliable algorithms are now available. The aim of the work reported in this paper has been to build on the initial SAR segmentation to produce a low-level description of the SAR scene and then to demonstrate the use of high-level processing applied to the low-level components. To this end, feature-based classification of segments into different terrain types has been implemented. Furthermore, algorithms for linear feature detection and classification have been developed. These use measures of length and thinness to find candidate starting segments from which networks of potential lines are grown using a Kalman filter to identify potential extensions to the current line whilst also providing a measure of confidence for the detected line. Once the image constituents have been identified with associated degrees of confidence, Bayesian techniques can be used to exploit prior contextual information. This is demonstrated with respect to the target detection application for which prior probabilities are introduced given terrain type, hedge proximity and proximity of other targets. It is shown how enhanced target detection can be obtained by utilising this contextual information in a rigorous statistical framework.