large-baseline insar for precise topographic mapping: a framework for tandem-x large-baseline data
Clicks: 135
ID: 174107
2017
The global Digital Elevation Model (DEM) resulting from the
TanDEM-X mission provides information about the world topography with
outstanding precision. In fact, performance analysis carried out with the
already available data have shown that the global product is well within the
requirements of 10 m absolute vertical accuracy and 2 m relative vertical
accuracy for flat to moderate terrain. The mission's science phase took place
from October 2014 to December 2015. During this phase, bistatic acquisitions
with across-track separation between the two satellites up to 3.6 km at the
equator were commanded. Since the relative vertical accuracy of InSAR derived
elevation models is, in principle, inversely proportional to the system
baseline, the TanDEM-X science phase opened the doors for the generation of
elevation models with improved quality with respect to the standard product.
However, the interferometric processing of the large-baseline data is
troublesome due to the increased volume decorrelation and very high frequency
of the phase variations. Hence, in order to fully profit from the increased
baseline, sophisticated algorithms for the interferometric processing, and,
in particular, for the phase unwrapping have to be considered. This paper
proposes a novel dual-baseline region-growing framework for the phase
unwrapping of the large-baseline interferograms. Results from two experiments
with data from the TanDEM-X science phase are discussed, corroborating the
expected increased level of detail of the large-baseline DEMs.
Reference Key |
pinheiro2017advanceslarge-baseline
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
---|---|
Authors | ;M. Pinheiro;A. Reigber;A. Moreira |
Journal | neuropsychiatrie : klinik, diagnostik, therapie und rehabilitation : organ der gesellschaft osterreichischer nervenarzte und psychiater |
Year | 2017 |
DOI | 10.5194/ars-15-231-2017 |
URL | |
Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.