PARISAR: Patch-Based Estimation and Regularized Inversion for Multibaseline SAR Interferometry

Giampaolo Ferraioli

doi:10.1109/TGRS.2017.2765761

Return

PARISAR: Patch-Based Estimation and Regularized Inversion for Multibaseline SAR Interferometry

2018-03-01

Giampaolo Ferraioli *

Charles‐Alban Deledalle

Denis, Loic

Florence Tupin

DOI：10.1109/TGRS.2017.2765761

Original

Save

View PDF

Abstract

En 中文

Reconstruction of elevation maps from a collection of synthetic aperture radar (SAR) images obtained in interferometric configuration is a challenging task. Reconstruction methods must overcome two difficulties: the strong interferometric noise that contaminates the data and the 2 pi phase ambiguities. Interferometric noise requires some form of smoothing among pixels of identical height. Phase ambiguities can be solved, up to a point, by combining linkage to the neighbors and a global optimization strategy to prevent from being trapped in local minima. This paper introduces a reconstruction method, PARISAR, that achieves both a resolution-preserving denoising and a robust phase unwrapping (PhU) by combining nonlocal denoising methods based on patch similarities and total-variation regularization. The optimization algorithm, based on graph cuts, identifies the global optimum. Combining patch-based speckle reduction methods and regularization-based PhU requires solving several issues: 1) computational complexity, the inclusion of nonlocal neighborhoods strongly increasing the number of terms involved during the regularization, and 2) adaptation to varying neighborhoods, patch comparison leading to large neighborhoods in homogeneous regions and much sparser neighborhoods in some geometrical structures. PARISAR solves both issues. We compare PARISAR with other reconstruction methods both on numerical simulations and satellite images and show a qualitative and quantitative improvement over state-of-the-art reconstruction methods for multibaseline SAR interferometry.

Keywords:

Multichannel interferometric synthetic aperture radar (SAR)

nonlocal means

SAR interferometry

total variation (TV) regularization

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.