Geoinformatics & Remote Sensing
Change Detection
Detecting change over time in bi- or multi-temporal multivariate spatial
data is a challenging and important task in many fields of application. Many
methods apply. When dealing with bi-temporal optical data we work with
methods that are insensitive to scaling (actually invariant to affine
transformations) within the two sets of variables. This is a huge advantage
since normalization and other corrections that are linear over time are not
needed.
Polarimetric radar data presents special problems when compared to optical
data. This can be dealt with by applying the complex Wishart distribution to
so-called multi-look radar data.
Present work in the area deals with regularization methods to avoid problems
that occur when the number of observations is small compared to the number
of variables. This may be the case for hyperspectral data. Also,
regularization may be desirable when the data are highly correlated as with
spectral or spatial data. more
Contact:
Allan Aasbjerg Nielsen
Estimation of Velocity Fields in Meteosat Image Sequences
The estimation of flow fields from time sequences of satellite imagery
has a number of important applications. For visualization of cloud or
sea ice movements in sequences of crude temporal sampling a satisfactory
non blurred temporal interpolation can be performed only when the flow
field or an estimate there-of is known. Estimated flow fields in weather
satellite imagery might also be used on an operational
basis as inputs to short-term weather prediction.
Local measurements of motion are obtained by analysis of
the local energy distribution, which is sampled using a set of
separable 3-D spatio-temporal Gabor filters evenly distributed
of all spatio-temporal directions.
The estimated local energy distribution also
allows us to compute a certainty measure of the estimated local flow.
We use Markovian random fields in order to
incorporate smoothness across the field. To obtain smoothness we
constrain first as well as second order derivatives of the flow
field.
more
Contact:
Rasmus Larsen
Enhancement and Analysis of Ocean Water Variability
Satellite altimetry has successfully been used to monitor the ocean surface
and has provided valuable information about the dynamics of the worlds
oceans and the marine gravity field. Other Earth observing sensors onboard
satellites have provided an enormous amount of information about the sea
surface temperature and the ocean colour. Through interdisciplinary projects
methods are developed for integrating multi mission, multi sensor and multi
channel satellite data for improved determination and analysis of the sea
level.
more
Contact:
Allan Aasbjerg Nielsen,
Klaus Baggesen Hilger
Numerical Weather Predictions for GPS Positioning
During transmission through the atmosphere the GPS satellite signals are
affected by the media. In the neutral atmosphere the refraction causes a
signal delay, which is a function of the meteorological conditions along
the signal path. Numerical weather predictions (NWP) are predictions of
the three dimensional meteorological conditions for a given area and point
in time, and can as such be used for predicting the delay for a satellite
signal by integration along the signal path through the NWP. This estimate
of the signal delay can be used in the positioning process to account for
the error caused by the delay, and the goal of the research is thus to
obtain improvements in positioning accuracy and reliability for high
accuracy GPS positioning. more
Contact:
Anna B.O. Jensen
Restoration of Polarimetric EMISAR Data
A core aspect of this project is investigation and development of
statistical methods and models for optimal use of polarimetric
EMISAR
data. Contextual information in an image is embedded not only
in the individual pixels but also in the relative position of neighbouring
pixel values. The potential of utilizing this relative position to explain the
structure underlying the EMISAR data is explored. The restorations are carried out
using Markov Random Fields in a Bayesian framework. The work is a
part of the multidisciplinary project named
DANMAC (DANish Multisensor Airborne Campaign) . The purpose of the DANMAC
project has been to achieve a better understanding of the physical
conditions and processes at or near the surface and their influence on the signals
registered by radar and optical, remote sensing sensors.
Contact:
Stefán Meulengracht Sørensen
The use of Polarimetric EMISAR for the Mapping and Characterization of the Semi-natural Environment
In the recent years the use of Earth-observing satellites has become
increasingly important in the monitoring of our planet. Synthetic
Aperture Radar (SAR) is a technology for collecting image data, based
upon active microwave remote sensing. Polarimetric SAR, represents some
of the most sophisticated and up-to-date developments in SAR remote
sensing, providing wide scope for research and application development work.
The general topic of this project is the application of polarimetric
EMISAR
data for mapping and characterization of semi-natural ecosystems.
The work is a part of the multidisciplinary project named
DANMAC (DANish Multisensor Airborne Campaign) . The purpose of the DANMAC
project has been to achieve a better understanding of the physical
conditions and processes at or near the surface and their influence on the signals
registered by radar and optical, remote sensing sensors.
Contact:
Stefán Meulengracht Sørensen