BMINSAR: A novel approach for InSAR phase denoising by clustering and block matching

We present a novel approach for phase denoising in Interferometric Synthetic Aperture Radar (InSAR) images, named as Block-Matching InSAR (BMInSAR). It uses k-means clustering to solve the block matching similarity search problem, thus simplifying preprocessing steps and filtering several reference-blocks at once. Also, we propose a novel methodology based on ground-truth GPS measurements to assess the filtering quality of Digital Elevation Models (DEMs) derived from a pair of Very High-Resolution (VHR) SAR complex images. Our dataset was obtained by X-Band airborne sensor OrbiSAR-2 from BRADAR. BMInSAR significantly outperforms the state-of-the-art filtering methods in both accuracy and execution time. After filtering with BMInSAR, we achieved an accuracy of 21cm in the resulting DEM of a homogeneous lawn area, which is quite similar to that obtained by LiDAR technology.