Edge detection and edge enhancement in SAR images is, due to the speckle effect, not so easily achieved. Here we consider edge enhancement as a classification problem, i.e. we segment an image in several edge classes and a no edge class. Thus supervised classification techniques become available. We propose an artificial neural network approach and interpret the output as a 'grade of being an edge pixel'. For training of the network we applied two training strategies: (1) selection of training samples from the data in a supervised way and (2) artificial creation of training samples based on speckle statistics using a speckle simulation algorithm. Both strategies are applied on a data set acquired by DLR's E-SAR in the L-band. The outputs of the edge enhancement process are compared among each other and with the RoA edge detector.
['Alexandros Dimou', 'Gunther Jäger', 'Panagiotis Frangos']
Adaptive edge enhancement in SAR images training on the data vs. training on simulated data