An Adaptive Multilooking Approach for a Small Number of SAR Images in Generating Multitemporal INSAR Set

Adaptive multilooking applied to multiple synthetic aperture radar (SAR) observations has been proven to be an effective process to improve the quality of multitemporal interferometric SAR (InSAR), in which the key task is to select the statistically homogeneous pixels (SHPs). The existing algorithms are mainly based on time-series information from the same position and perform unsatisfactorily when image number is small. In this study, we propose an adaptive multilooking approach based on the context covariance matrix for SHP selection, named CCM-SHPS. The core idea is to exploit spatially adjacent pixels to enhance the information volume. The context covariance matrix is constructed and the Wishart statistic test is employed to measure the similarity. The proposed CCM-SHPS is validated by a simulated stack on the filtered InSAR results, including the amplitude, interferometric phase, and coherence, demonstrating its advantage over five representative algorithms in speckle suppression and edge preservation.