Correlation and similarity measures for SAR image matching

Abstract

SAR image matching is a difficult task in relief reconstruction by radargrammetry. Two major class of methods exist : Area based methods and feature based methods. In one hand, feature based methods can give robust, but sparse disparity maps. The difficulty lies in feature extraction. On the other hand, area based methods give dense disparity maps but classical correlation measures are not efficient because of speckle noise. In this paper we deal with various correlation measures evaluation. We propose and compare different ways to estimate the correlation, or the similarity between a couple of SAR image in radargrammetric conditions. Five correlation coefficients will be studied : - the classical Zero Normalized Correlation Coefficient (ZNCC), - a ZNCC applied on a edge image of the scene, - a Binary Correlation Coefficient : we define a binary image of both images and measure the binary overlap. - a correlation coefficient taking into account the Intensity Image statistics, - a correlation coefficient taking into account the Reflectivity Correlation of the underlying scene. We also introduce two similarity measures : - the Cluster Reward Algorithm - and the Mutual Information These kind of operators are based on an entropy measure, and a 2D-histogram analysis to estimate the similarity between the couple of image. They are well adapted to compare images with different radiometry, but similar geometry. In our work, we evaluate the performances of these coefficients with SAR images. We also characterize their behavior on different kind of scenes (textured, high relief area, cities...).