Multi-scale and block decomposition methods for finite difference integration and phase unwrapping of very large datasets in high resolution SAR interferometry

In the last few years high and very high resolution SAR data have become available, opening new possibilities in the field of SAR interferometry. However, the huge amount of data poses new challenges in terms of computational and memory requirements, in particular to those processing steps that require a global approach to obtain good results, such as elevation or velocity finite difference integration and phase unwrapping. In this paper we propose two approaches to overcome this problem. In the first approach the data to be processed are divided in blocks of smaller size, and different strategies are suggested to make the solutions of the different blocks consistent. The second approach is based on a decomposition of the problem at different scales according to a pyramidal scheme, which makes possible to exploit the available information at a global level (thus guaranteeing optimal quality results) with scalable computational demand. The proposed approaches were successfully tested on high resolution COSMO-SkyMed full frame data.