Fast threshold selection algorithm for segmentation of synthetic aperture radar images

Abstract

Automatic detection of disjoint regions in synthetic aperture radar (SAR) images for applications requiring localisation and identification of objects is complicated by the nature of the speckle. A multi-level ratio of exponential weighted averages (MROEWA) together with a fast algorithm for optimal threshold selection is proposed for SAR segmentation. A non-parametric and unsupervised principle using the grey level histogram is utilised for producing the regions that are homogenous. An optimal threshold is automatically selected by maximising the separability of the classes in grey level by incorporating a simple search strategy. Experimental results on both synthetic and real images verify the effectiveness of the proposed method. It is validated that the proposed method outperforms greatly by reducing the number of arithmetic operations required for computing the optimal threshold.