Edge detection efficiency in single-look SAR images by elementary and neural network based detectors

Synthetic aperture radars (SARs) are known to be extremely useful in many applications of modern remote sensing (RS) [1]. However, an essential drawback of images acquired by SARs is the presence of intensive noise-like phenomenon called speckle which has multiplicative nature and non-Gaussian distribution law [1]. While single-look imaging mode provides the best spatial resolution, it is simultaneously characterized by the most intensive speckle with obvious non-Gaussianity. This causes problems in solving many typical image processing tasks as pre-filtering, object detection, and segmentation. Heterogeneity (in particular, edge) detection is a standard operation employed for the aforementioned tasks [1, 2]. Thus, efficient detection of edges can serve as a good pre-requisite for further processing of SAR images.