An Unsupervised Generative Neural Approach for InSAR Phase Filtering and Coherence Estimation

Phase filtering and pixel quality (coherence) estimation is critical in producing Digital Elevation Models (DEMs) from Interferometric Synthetic Aperture Radar (InSAR) images, as it removes spatial inconsistencies (residues) and immensely improves the subsequent unwrapping. Large amount of InSAR data facilitates Wide Area Monitoring (WAM) over geographical regions. Advances in parallel computing have accelerated Convolutional Neural Networks (CNNs), giving them advantages over human performance on visual pattern recognition, which makes CNNs a good choice for WAM. Nevertheless, this research is largely unexplored. We thus propose "GenInSAR", a CNN-based generative model for joint phase filtering and coherence estimation, that directly learns the InSAR data distribution. GenInSAR's unsupervised training on satellite and simulated noisy InSAR images outperforms other five related methods in total residue reduction (over 16.5% better on average) with less over-smoothing/artefacts around branch cuts. GenInSAR's Phase, and Coherence Root-Mean-Squared-Error and Phase Cosine Error have average improvements of 0.54, 0.07, and 0.05 respectively compared to the related methods.Comment: to be published in a future issue of IEEE Geoscience and Remote Sensing Letter

Nonlocal noise reduction method based on fringe frequency compensation for SAR interferogram

Phase noise reduction is one of the key steps for synthetic aperture radar interferometry data processing. In this article, a novel phase filtering method is proposed. The main innovation and contribution of this research is to 1) incorporate local fringe frequency (LFF) compensation technique into the nonlocal phase filtering method to include more independent and identically distributed samples for filtering; 2) modify the nonlocal phase filter from three aspects: 1) executing nonlocal filtering in the complex domain of the residual phase to avoid gray jumps in phase, 2) adaptively calculating the smoothing parameter based on the LFF and the coherence coefficient, and 3) using the integral image in similarity calculation to improve the efficiency; 3) perform Goldstein filter in high coherence areas to reduce the computation expense. Experiments based on both simulated and real data have shown that the proposed method has achieved a better performance in terms of both noise reduction and edge preservation than some existing phase filtering methods

A Nonlocal InSAR Filter for High-Resolution DEM Generation From TanDEM-X Interferograms

This paper presents a nonlocal interferometric synthetic aperture radar (InSAR) filter with the goal of generating digital elevation models (DEMs) of higher resolution and accuracy from bistatic TanDEM-X strip map interferograms than with the processing chain used in production. The currently employed boxcar multilooking filter naturally decreases the resolution and has inherent limitations on what level of noise reduction can be achieved. The proposed filter is specifically designed to account for the inherent diversity of natural terrain by setting several filtering parameters adaptively. In particular, it considers the local fringe frequency and scene heterogeneity, ensuring proper denoising of interferograms with considerable underlying topography as well as urban areas. A comparison using synthetic and TanDEM-X bistatic strip map data sets with existing InSAR filters shows the effectiveness of the proposed techniques, most of which could readily be integrated into existing nonlocal filters. The resulting DEMs outclass the ones produced with the existing global TanDEM-X DEM processing chain by effectively increasing the resolution from 12 to 6 m and lowering the noise level by roughly a factor of two