1,145 research outputs found
Non-Local Compressive Sensing Based SAR Tomography
Tomographic SAR (TomoSAR) inversion of urban areas is an inherently sparse
reconstruction problem and, hence, can be solved using compressive sensing (CS)
algorithms. This paper proposes solutions for two notorious problems in this
field: 1) TomoSAR requires a high number of data sets, which makes the
technique expensive. However, it can be shown that the number of acquisitions
and the signal-to-noise ratio (SNR) can be traded off against each other,
because it is asymptotically only the product of the number of acquisitions and
SNR that determines the reconstruction quality. We propose to increase SNR by
integrating non-local estimation into the inversion and show that a reasonable
reconstruction of buildings from only seven interferograms is feasible. 2)
CS-based inversion is computationally expensive and therefore barely suitable
for large-scale applications. We introduce a new fast and accurate algorithm
for solving the non-local L1-L2-minimization problem, central to CS-based
reconstruction algorithms. The applicability of the algorithm is demonstrated
using simulated data and TerraSAR-X high-resolution spotlight images over an
area in Munich, Germany.Comment: 10 page
A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR
regularization is used for finding sparse solutions to an
underdetermined linear system. As sparse signals are widely expected in remote
sensing, this type of regularization scheme and its extensions have been widely
employed in many remote sensing problems, such as image fusion, target
detection, image super-resolution, and others and have led to promising
results. However, solving such sparse reconstruction problems is
computationally expensive and has limitations in its practical use. In this
paper, we proposed a novel efficient algorithm for solving the complex-valued
regularized least squares problem. Taking the high-dimensional
tomographic synthetic aperture radar (TomoSAR) as a practical example, we
carried out extensive experiments, both with simulation data and real data, to
demonstrate that the proposed approach can retain the accuracy of second order
methods while dramatically speeding up the processing by one or two orders.
Although we have chosen TomoSAR as the example, the proposed method can be
generally applied to any spectral estimation problems.Comment: 11 pages, IEEE Transactions on Geoscience and Remote Sensin
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
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
The worsening impacts of land reclamation assessed with Sentinel-1: The Rize (Turkey) test case
Massive amounts of land are being reclaimed to build airports, new cities, ports, and highways. Hundreds of kilometers are added each year, as coastlines are extended further out to the sea. In this paper, this urbanization approach is monitored by Persistent Scatterer Interferometry (PSI) technique with Sentinel-1 SAR data. The study aims to explore this technology in order to support local authorities to detect and evaluate subtle terrain displacements. For this purpose, a large 3-years Sentinel-1 stack composed by 92 images acquired between 07/01/2015 to 27/01/2018 is employed and stacking techniques are chosen to assess ground motion. The test site of this study, Rize, Turkey, has been declared at high risk of collapse and radical solutions such as the relocation of the entire city in another area are been taken into consideration. A media fact-checking approach, i.e. evaluating national and international press releases on the test site, is considered for the paper and this work presents many findings in different areas of the city. For instance, alerts are confirmed by inspecting several buildings reported by the press. Critical infrastructures are monitored as well. Portions of the harbor show high displacement rates, up to 1âŻcm/year, proving reported warnings. Rural villages belonging to the same municipality are also investigated and a mountainous village affected by landslide is considered in the study. Sentinel-1 is demonstrated to be a suitable system to detect and monitor small changes or buildings and infrastructures for these scenarios. These changes may be highly indicative of imminent damage which can lead to the loss of the structural integrity and subsequent failure of the structure in the long-term. In Rize, only a few known motion-critical structures are monitored daily with in-situ technologies. SAR interferometry can assist to save expensive inspection and monitoring services, especially in highly critical cases such as the one studied in this paper
Assessment of high resolution SAR imagery for mapping floodplain water bodies: a comparison between Radarsat-2 and TerraSAR-X
Flooding is a world-wide problem that is considered as one of the most devastating natural hazards. New commercially available high spatial resolution Synthetic Aperture RADAR satellite imagery provides new potential for flood mapping. This research provides a quantitative assessment of high spatial resolution RADASAT-2 and TerraSAR-X products for mapping water bodies in order to help validate products that can be used to assist flood disaster management. An area near Dhaka in Bangladesh is used as a test site because of the large number of water bodies of different sizes and its history of frequent flooding associated with annual monsoon rainfall. Sample water bodies were delineated in the field using kinematic differential GPS to train and test automatic methods for water body mapping. SAR sensors products were acquired concurrently with the field visits; imagery were acquired with similar polarization, look direction and incidence angle in an experimental design to evaluate which has best accuracy for mapping flood water extent.
A methodology for mapping water areas from non-water areas was developed based on radar backscatter texture analysis. Texture filters, based on Haralick occurrence and co-occurrence measures, were compared and images classified using supervised, unsupervised and contextual classifiers. The evaluation of image products is based on an accuracy assessment of error matrix method using randomly selected ground truth data. An accuracy comparison was performed between classified images of both TerraSAR-X and Radarsat-2 sensors in order to identify any differences in mapping floods. Results were validated using information from field inspections conducted in good conditions in February 2009, and applying a model-assisted difference estimator for estimating flood area to derive Confidence Interval (CI) statistics at the 95% Confidence Level (CL) for the area mapped as water. For Radarsat-2 Ultrafine, TerraSAR-X Stripmap and Spotlight imagery, overall classification accuracy was greater than 93%. Results demonstrate that small water bodies down to areas as small as 150mÂČ can be identified routinely from 3 metre resolution SAR imagery. The results further showed that TerraSAR-X stripmap and spotlight images have better overall accuracy than RADARSAT-2 ultrafine beam modes images. The expected benefits of the research will be to improve the provision of data to assess flood risk and vulnerability, thus assisting in disaster management and post-flood recovery
First TerraSAR-X interferometry evaluation
The German radar satellite TerraSAR-X was launched
in June 2007 [1] and is currently ending its
commissioning phase. We anticipate quite different
interferometric application scenarios compared to ERS-
1/2 and ASAR due to the X-band frequency, the short
orbital repeat cycles of 11 days, the high range
resolution and the spotlight mode of this sensor.
During the commissioning phase we have scheduled a
number of acquisitions over selected test sites with
different characteristics to get an early quick look of
TerraSAR-X's interferometric capabilities and to assess
the phase quality of the sensor and DLRâs processor
system [2].
Our first results are quite encouraging and the technical
parameters of the system are as specified. Many
spectacular image details let us expect that the high
resolution will demand a different view on SAR
interferometry and allow new applications in urban
environments.
In our paper we show interferograms and images of
different test sites, coherence measurements and a first
assessment of the interferometric properties. We will
give hints to future scientific users on data selection and
data processing.
The results are of high relevance for the TanDEM-X
mission scheduled for 2009, when a second compatible
SAR-sensor will be launched for a joint 3 year bistatic
interferometric formation flight
Online Îvaluation of Earth Observation Derived Indicators for Urban Planning and Management
Extensive urbanization and growth of population density have acquired a paramount interest towards a sustainable urban development. Earth Observation (EO) is an important source of information required for urban planning and management. The availability of EO data provides the immense opportunity for urban environmental indicators development easily derived by remote sensors. In this study, the state of the art methods were employed to develop urban planning and management relevant indicators that can be evaluated by using EO data. The importance of this approach lies on providing alternatives for improving urban planning and management, without consuming time and resources in collecting field or archived data. The evaluated urban indicators were integrated into a Webâbased Information System that was developed for online exploitation. The results for three case studies are therefore available online and can be used by urban planners and stakeholders in supporting their planning decisions
ANALYSIS OF X-BAND VERY HIGH RESOLUTION PERSISTENT SCATTERER INTERFEROMETRY DATA OVER URBAN AREAS
Persistent Scatterer Interferometry (PSI) is a satellite-based Synthetic Aperture Radar (SAR) remote sensing technique used to measure and monitor land deformation from a stack of interferometric SAR images. This work concerns X-band PSI and, in particular, PSI based on very high resolution (VHR) StripMap CosmoSkyMed and TerraSAR-X SAR imagery. In fact, it mainly focuses on the technical aspects of deformation measurement and monitoring over urban areas. A key technical aspect analysed in this paper is the thermal expansion component of PSI observations, which is a result of temperature differences in the imaged area between SAR acquisitions. This component of PSI observations is particularly important in the urban environment. This is an interesting feature of PSI, which can be surely used to illustrate the high sensitivity of X-band PSI to very subtle displacements. Thermal expansion can have a strong impact on the PSI products, especially on the deformation velocity maps and deformation time series, if not properly handled during the PSI data processing and analysis, and a comprehensive discussion of this aspect will be provided in this paper. The importance of thermal expansion is related to the fact that the PSI analyses are often performed using limited stacks of images, which may cover a limited time period, e.g. several months only. These two factors (limited number of images and short period) make the impact of a non-modelled thermal expansion particularly critical. This issue will be illustrated considering different case studies based on TerraSAR-X and CosmoSkyMed PSI data. Besides, an extended PSI model which alleviates this problem will be described and case studies from the Barcelona metropolitan area will demonstrate the effectiveness of the proposed strategy
Post-failure evolution analysis of a rainfall-triggered landslide by multi-temporal interferometry SAR approaches integrated with geotechnical analysis
Persistent Scatterers Interferometry (PSI) represents one of the most powerful techniques for Earth's surface deformation processes' monitoring, especially for long-term evolution phenomena. In this work, a dataset of 34 TerraSAR-X StripMap images (October 2013âOctober 2014) has been processed by two PSI techniques - Coherent Pixel Technique-Temporal Sublook Coherence (CPT-TSC) and Small Baseline Subset (SBAS) - in order to study the evolution of a slow-moving landslide which occurred on February 23, 2012 in the Papanice hamlet (Crotone municipality, southern Italy) and induced by a significant rainfall event (185 mm in three days). The mass movement caused structural damage (buildings' collapse), and destruction of utility lines (gas, water and electricity) and roads. The results showed analogous displacement rates (30â40 mm/yr along the Line of Sight â LOS-of the satellite) with respect to the pre-failure phase (2008â2010) analyzed in previous works. Both approaches allowed detect the landslide-affected area, however the higher density of targets identified by means of CPT-TSC enabled to analyze in detail the slope behavior in order to design possible mitigation interventions. For this aim, a slope stability analysis has been carried out, considering the comparison between groundwater oscillations and time-series of displacement. Hence, the crucial role of the interaction between rainfall and groundwater level has been inferred for the landslide triggering. In conclusion, we showed that the integration of geotechnical and remote sensing approaches can be seen as the best practice to support stakeholders to design remedial works.Peer ReviewedPostprint (author's final draft
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