2018
DOI: 10.1109/tgrs.2018.2839027
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A Nonlocal InSAR Filter for High-Resolution DEM Generation From TanDEM-X Interferograms

Abstract: This is a preprint, to read the final version please go to IEEE Transactions on Geoscience and Remote Sensing on IEEE XPlore. This paper presents a nonlocal InSAR filter with the goal of generating digital elevation models 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 achie… Show more

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Cited by 32 publications
(19 citation statements)
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“…In practice, the multilinear rank and the 0 norm are relaxed by the tensor nuclear norm X * and 1 norm, respectively. RoMIO reaches filtering performance comparable to state-of-the-art filtering algorithms, i.e., nonlocal means filtering [59,61]. However, it outperforms nonlocal means filtering by a factor of two in terms of the interferometric phase variance when the interferogram is corrupted by 50% outliers [63].…”
Section: Romiomentioning
confidence: 91%
See 1 more Smart Citation
“…In practice, the multilinear rank and the 0 norm are relaxed by the tensor nuclear norm X * and 1 norm, respectively. RoMIO reaches filtering performance comparable to state-of-the-art filtering algorithms, i.e., nonlocal means filtering [59,61]. However, it outperforms nonlocal means filtering by a factor of two in terms of the interferometric phase variance when the interferogram is corrupted by 50% outliers [63].…”
Section: Romiomentioning
confidence: 91%
“…In practice, we are often faced with a limited number of images. In such situations, a proper algorithm should exploit information from neighboring pixels in order to reduce the number of images needed for a reliable reconstruction, such as adaptive filtering and nonlocal filtering that have been extensively described in previous literature, such as [4,57,58] and [59][60][61], respectively. However, this section goes beyond these pixel cluster-based methods.…”
Section: Object-based Insar Algorithmsmentioning
confidence: 99%
“…In [24], improvements are proposed through the compensation of the deterministic, topographic phase component, leading to a better selection of the statistically homogeneous pixels for high resolution DEM generation. A similar idea is developed in [25].…”
Section: Interferometric and Tomographic Datamentioning
confidence: 99%
“…The rapid developed synthetic aperture radar (SAR) techniques, especially the high-resolution interferometric SAR (InSAR) make it possible to observe more detail information of the targets in many remote sensing activities. Herein, the interferometric phase shows its great power in many InSAR remote sensing applications [1]- [3]. However, the existence of the noise in the InSAR phase which introduced by several factors, such as thermal noise, different decorrelation, SAR image coregistration, will affect the InSAR data processing and following application.…”
Section: Introductionmentioning
confidence: 99%