Neural network based edge detection in two-look and dual-polarization radar images

Edge detection is a standard operation in image processing. It becomes problematic if noise is not additive, not Gaussian and not i.i.d. as this happens in images acquired by synthetic aperture radar (SAR). To perform edge detection better, it has been recently proposed to apply a trained neural network (NN) and SAR image pre-filtering for single-look mode. In this paper, we demonstrate that the proposed detector is, after certain modifications, applicable for edge detection in two-look and dual-polarization SAR images with and without pre-filtering. Moreover, we show that a recently introduced parameter AUC (Area Under the Curve) can be helpful in optimization of parameters for elementary edge detectors used as inputs of the NN edge detector. Quantitative analysis results confirming efficiency of the proposed detector are presented. Its performance is also studied for real-life TerraSAR-X data.