Local residue coupling strategies by neural network for InSAR phase unwrapping

Phase unwrapping is one of the toughest problems in interferometric SAR processing. The main difficulties arise from the presence of pointlike error sources, called residues, which occur mainly in close couples due to phase noise. We present an assessment of a local approach to the resolution of these problems by means of a neural network. Using a Multi-Layer Perceptron, trained with the back-propagation scheme on a series of simulated phase images, we exploit the information contained in the phase gradient field neighboring a given site to extract in a probabilistic fashion the best pairing strategies for close residue couples. Results show that good efficiencies and accuracies can be obtained, provided a sufficient number of training examples are supplied. The technique is tested also on real SAR ERS-1/2 tandem interferometric images of the Matera (southern Italy) test site, showing a good reduction of the residue density. The better results obtained by use of the neural network as far as local criteria are adopted appear justified given the probabilistic nature of the noise process on SAR interferometric phase fields and allows to outline a specifically tailored implementation of the neural network approach as a very fast pre-processing step intended to decrease the residue density and give sufficiently clean images to be processed further by more conventional techniques.