A new phase unwrapping method based on region recognition and region expansion

摘要

Phase unwrapping is a key step in retrieving digital elevation models (DEMs) from across-track interferometric synthetic aperture radar (InSAR) data. The coherence of synthetic aperture radar (SAR) data set is an effective indicator for the quality of phase unwrapping. However, the coherence of different regions usually distributes unevenly in SAR images monitoring heterogeneous areas. Errors in low-coherence areas are prone to pollute the whole image. In order to mitigate propagation error, a new phase unwrapping algorithm based on region recognition and region expansion is proposed. In the region recognition step, optical images are incorporated to recognize low-coherence regions by virtue of supervised classification technique. Low-coherence regions and the ones that are not of interest for the application are then discarded. In the region expansion step, stable pixels of high coherence are selected as growing seeds, and then phase unwrapping grows from high-quality regions to low-quality ones guided by coherence information and weighted numbers of neighbouring unwrapped pixels. The ambiguity number of a wrapped pixel is estimated from its neighbouring pixels under the criteria of pixel distance and phase gradient. Iterative examination continues until the whole image is unwrapped. Experiments on PALSAR and ASAR data demonstrate its validity and advantages over other classical methods.