Variance ratio for change detection in SAR imagery

Abstract

Change detection provides a powerful means for the initial detection of small target objects. However, speckle effects mean this type of approach can be difficult to apply to Synthetic Aperture Radar (SAR) imagery. This paper examines one method for target detection using change between a registered pair of SAR images. The technique may be parameterized to detect small target objects ranging in size from a few to perhaps a few hundred pixels. The approach considered here exploits the observation that the scattering response of many target types of interest is dominated by a small number of bright scatterers, whilst natural clutter regions tend not to display this property. The variance provides a useful statistic summarizing this effect, consequently the detection method considered here is based on the ratio of the variances of corresponding patches in the pair of images. Ideally any test statistic should be characterized by a known statistical distribution; this will allow formal tests of a null hypothesis to be carried out. Here the null hypothesis corresponds to no change, and knowledge of the distribution of the test statistic enables the implementation of a Constant False-Alarm Rate (CFAR) detection process. The analysis carried out herein considers the distribution of the variance ratio under realistic operating parameterisations for target detection in SAR imagery. Synthetic data is used to characterize this distribution, and Monte Carlo techniques are applied to derive empirical formulae for use in an online application. Results are presented for synthetic data and for a registered image pair, in the form of detection maps.