4,673 research outputs found
Long-Time Behavior of Quasilinear Thermoelastic Kirchhoff-Love Plates with Second Sound
We consider an initial-boundary-value problem for a thermoelastic Kirchhoff &
Love plate, thermally insulated and simply supported on the boundary,
incorporating rotational inertia and a quasilinear hypoelastic response, while
the heat effects are modeled using the hyperbolic Maxwell-Cattaneo-Vernotte law
giving rise to a 'second sound' effect. We study the local well-posedness of
the resulting quasilinear mixed-order hyperbolic system in a suitable solution
class of smooth functions mapping into Sobolev -spaces. Exploiting the
sole source of energy dissipation entering the system through the hyperbolic
heat flux moment, provided the initial data are small in a lower topology
(basic energy level corresponding to weak solutions), we prove a nonlinear
stabilizability estimate furnishing global existence & uniqueness and
exponential decay of classical solutions.Comment: 46 page
Fusion of Urban TanDEM-X raw DEMs using variational models
Recently, a new global Digital Elevation Model (DEM) with pixel spacing of
0.4 arcseconds and relative height accuracy finer than 2m for flat areas
(slopes 20%) was created
through the TanDEM-X mission. One important step of the chain of global DEM
generation is to mosaic and fuse multiple raw DEM tiles to reach the target
height accuracy. Currently, Weighted Averaging (WA) is applied as a fast and
simple method for TanDEM-X raw DEM fusion in which the weights are computed
from height error maps delivered from the Interferometric TanDEM-X Processor
(ITP). However, evaluations show that WA is not the perfect DEM fusion method
for urban areas especially in confrontation with edges such as building
outlines. The main focus of this paper is to investigate more advanced
variational approaches such as TV-L1 and Huber models. Furthermore, we also
assess the performance of variational models for fusing raw DEMs produced from
data takes with different baseline configurations and height of ambiguities.
The results illustrate the high efficiency of variational models for TanDEM-X
raw DEM fusion in comparison to WA. Using variational models could improve the
DEM quality by up to 2m particularly in inner-city subsets.Comment: This is the pre-acceptance version, to read the final version, please
go to IEEE Journal of Selected Topics in Applied Earth Observations and
Remote Sensing on IEEE Xplor
Towards Automatic SAR-Optical Stereogrammetry over Urban Areas using Very High Resolution Imagery
In this paper we discuss the potential and challenges regarding SAR-optical
stereogrammetry for urban areas, using very-high-resolution (VHR) remote
sensing imagery. Since we do this mainly from a geometrical point of view, we
first analyze the height reconstruction accuracy to be expected for different
stereogrammetric configurations. Then, we propose a strategy for simultaneous
tie point matching and 3D reconstruction, which exploits an epipolar-like
search window constraint. To drive the matching and ensure some robustness, we
combine different established handcrafted similarity measures. For the
experiments, we use real test data acquired by the Worldview-2, TerraSAR-X and
MEMPHIS sensors. Our results show that SAR-optical stereogrammetry using VHR
imagery is generally feasible with 3D positioning accuracies in the
meter-domain, although the matching of these strongly hetereogeneous
multi-sensor data remains very challenging. Keywords: Synthetic Aperture Radar
(SAR), optical images, remote sensing, data fusion, stereogrammetr
Observation of topological transition of Fermi surface from a spindle-torus to a torus in large bulk Rashba spin-split BiTeCl
The recently observed large Rashba-type spin splitting in the BiTeX (X = I,
Br, Cl) bulk states due to the absence of inversion asymmetry and large charge
polarity enables observation of the transition in Fermi surface topology from
spindle-torus to torus with varying the carrier density. These BiTeX systems
with high spin-orbit energy scales offer an ideal platform for achieving
practical spintronic applications and realizing non-trivial phenomena such as
topological superconductivity and Majorana fermions. Here we use Shubnikov-de
Haas oscillations to investigate the electronic structure of the bulk
conduction band of BiTeCl single crystals with different carrier densities. We
observe the topological transition of the Fermi surface (FS) from a
spindle-torus to a torus. The Landau level fan diagram reveals the expected
non-trivial {\pi} Berry phase for both the inner and outer FSs. Angle-dependent
oscillation measurements reveal three-dimensional FS topology when the Fermi
level lies in the vicinity of the Dirac point. All the observations are
consistent with large Rashba spin-orbit splitting in the bulk conduction band.Comment: 28 pages, supplementary informatio
A Framework for SAR-Optical Stereogrammetry over Urban Areas
Currently, numerous remote sensing satellites provide a huge volume of
diverse earth observation data. As these data show different features regarding
resolution, accuracy, coverage, and spectral imaging ability, fusion techniques
are required to integrate the different properties of each sensor and produce
useful information. For example, synthetic aperture radar (SAR) data can be
fused with optical imagery to produce 3D information using stereogrammetric
methods. The main focus of this study is to investigate the possibility of
applying a stereogrammetry pipeline to very-high-resolution (VHR) SAR-optical
image pairs. For this purpose, the applicability of semi-global matching is
investigated in this unconventional multi-sensor setting. To support the image
matching by reducing the search space and accelerating the identification of
correct, reliable matches, the possibility of establishing an epipolarity
constraint for VHR SAR-optical image pairs is investigated as well. In
addition, it is shown that the absolute geolocation accuracy of VHR optical
imagery with respect to VHR SAR imagery such as provided by TerraSAR-X can be
improved by a multi-sensor block adjustment formulation based on rational
polynomial coefficients. Finally, the feasibility of generating point clouds
with a median accuracy of about 2m is demonstrated and confirms the potential
of 3D reconstruction from SAR-optical image pairs over urban areas.Comment: This is the pre-acceptance version, to read the final version, please
go to ISPRS Journal of Photogrammetry and Remote Sensing on ScienceDirec
Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset
As a unique classification scheme for urban forms and functions, the local
climate zone (LCZ) system provides essential general information for any
studies related to urban environments, especially on a large scale. Remote
sensing data-based classification approaches are the key to large-scale mapping
and monitoring of LCZs. The potential of deep learning-based approaches is not
yet fully explored, even though advanced convolutional neural networks (CNNs)
continue to push the frontiers for various computer vision tasks. One reason is
that published studies are based on different datasets, usually at a regional
scale, which makes it impossible to fairly and consistently compare the
potential of different CNNs for real-world scenarios. This study is based on
the big So2Sat LCZ42 benchmark dataset dedicated to LCZ classification. Using
this dataset, we studied a range of CNNs of varying sizes. In addition, we
proposed a CNN to classify LCZs from Sentinel-2 images, Sen2LCZ-Net. Using this
base network, we propose fusing multi-level features using the extended
Sen2LCZ-Net-MF. With this proposed simple network architecture and the highly
competitive benchmark dataset, we obtain results that are better than those
obtained by the state-of-the-art CNNs, while requiring less computation with
fewer layers and parameters. Large-scale LCZ classification examples of
completely unseen areas are presented, demonstrating the potential of our
proposed Sen2LCZ-Net-MF as well as the So2Sat LCZ42 dataset. We also
intensively investigated the influence of network depth and width and the
effectiveness of the design choices made for Sen2LCZ-Net-MF. Our work will
provide important baselines for future CNN-based algorithm developments for
both LCZ classification and other urban land cover land use classification
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