391 research outputs found
On the use of the l(2)-norm for texture analysis of polarimetric SAR data
In this paper, the use of the l2-norm, or Span, of the scattering vectors is suggested for texture analysis of polarimetric synthetic aperture radar (SAR) data, with the benefits that we need neither an analysis of the polarimetric channels separately nor a filtering of the data to analyze the statistics. Based on the product model, the distribution of the l2-norm is studied. Closed expressions of the probability density functions under the assumptions of several texture distributions are provided. To utilize the statistical properties of the l2-norm, quantities including normalized moments and log-cumulants are derived, along with corresponding estimators and estimation variances. Results on both simulated and real SAR data show that the use of statistics based on the l2-norm brings advantages in several aspects with respect to the normalized intensity moments and matrix variate log-cumulants.Peer ReviewedPostprint (published version
Breaking new ground in mapping human settlements from space -The Global Urban Footprint-
Today 7.2 billion people inhabit the Earth and by 2050 this number will have
risen to around nine billion, of which about 70 percent will be living in
cities. Hence, it is essential to understand drivers, dynamics, and impacts of
the human settlements development. A key component in this context is the
availability of an up-to-date and spatially consistent map of the location and
distribution of human settlements. It is here that the Global Urban Footprint
(GUF) raster map can make a valuable contribution. The new global GUF binary
settlement mask shows a so far unprecedented spatial resolution of 0.4 arcsec
() that provides - for the first time - a complete picture of the
entirety of urban and rural settlements. The GUF has been derived by means of a
fully automated processing framework - the Urban Footprint Processor (UFP) -
that was used to analyze a global coverage of more than 180,000 TanDEM-X and
TerraSAR-X radar images with 3m ground resolution collected in 2011-2012.
Various quality assessment studies to determine the absolute GUF accuracy based
on ground truth data on the one hand and the relative accuracies compared to
established settlements maps on the other hand, clearly indicate the added
value of the new global GUF layer, in particular with respect to the
representation of rural settlement patterns. Generally, the GUF layer achieves
an overall absolute accuracy of about 85\%, with observed minima around 65\%
and maxima around 98 \%. The GUF will be provided open and free for any
scientific use in the full resolution and for any non-profit (but also
non-scientific) use in a generalized version of 2.8 arcsec ().
Therewith, the new GUF layer can be expected to break new ground with respect
to the analysis of global urbanization and peri-urbanization patterns,
population estimation or vulnerability assessment
Guided patch-wise nonlocal SAR despeckling
We propose a new method for SAR image despeckling which leverages information
drawn from co-registered optical imagery. Filtering is performed by plain
patch-wise nonlocal means, operating exclusively on SAR data. However, the
filtering weights are computed by taking into account also the optical guide,
which is much cleaner than the SAR data, and hence more discriminative. To
avoid injecting optical-domain information into the filtered image, a
SAR-domain statistical test is preliminarily performed to reject right away any
risky predictor. Experiments on two SAR-optical datasets prove the proposed
method to suppress very effectively the speckle, preserving structural details,
and without introducing visible filtering artifacts. Overall, the proposed
method compares favourably with all state-of-the-art despeckling filters, and
also with our own previous optical-guided filter
Image Restoration for Remote Sensing: Overview and Toolbox
Remote sensing provides valuable information about objects or areas from a
distance in either active (e.g., RADAR and LiDAR) or passive (e.g.,
multispectral and hyperspectral) modes. The quality of data acquired by
remotely sensed imaging sensors (both active and passive) is often degraded by
a variety of noise types and artifacts. Image restoration, which is a vibrant
field of research in the remote sensing community, is the task of recovering
the true unknown image from the degraded observed image. Each imaging sensor
induces unique noise types and artifacts into the observed image. This fact has
led to the expansion of restoration techniques in different paths according to
each sensor type. This review paper brings together the advances of image
restoration techniques with particular focuses on synthetic aperture radar and
hyperspectral images as the most active sub-fields of image restoration in the
remote sensing community. We, therefore, provide a comprehensive,
discipline-specific starting point for researchers at different levels (i.e.,
students, researchers, and senior researchers) willing to investigate the
vibrant topic of data restoration by supplying sufficient detail and
references. Additionally, this review paper accompanies a toolbox to provide a
platform to encourage interested students and researchers in the field to
further explore the restoration techniques and fast-forward the community. The
toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS
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