77 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
Advances in mapping ice-free surfaces within the Northern Antarctic peninsula region using polarimetric RADARSAT-2 data
Ice-free areas within the Northern Antarctic Peninsula
region are of interest for studying changes occurring to
surface covers, including those related to glacial coverage,
raised beach deposits and periglacial processes and
permafrost. The objective of this work is to map the main
surface covers within ice-free areas of King George Island,
the largest island of the South Shetlands archipelago, using
fully polarimetric RADARSAT-2 SAR data.
Surface covers such as rock outcrops and glacial till, stone
fields, patterned ground, and sand and gravel deposits form
the most representative classes and account for 84 km2 of
the ice-free areas on the island. A distribution of complex
geomorphological features and landforms was obtained,
being some of them considered indicators of periglacial
processes and presence of permafrost.Published versio
Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks
This paper addresses the highly challenging problem of automatically
detecting man-made structures especially buildings in very high resolution
(VHR) synthetic aperture radar (SAR) images. In this context, the paper has two
major contributions: Firstly, it presents a novel and generic workflow that
initially classifies the spaceborne TomoSAR point clouds generated by
processing VHR SAR image stacks using advanced interferometric techniques known
as SAR tomography (TomoSAR) into buildings and non-buildings with the aid
of auxiliary information (i.e., either using openly available 2-D building
footprints or adopting an optical image classification scheme) and later back
project the extracted building points onto the SAR imaging coordinates to
produce automatic large-scale benchmark labelled (buildings/non-buildings) SAR
datasets. Secondly, these labelled datasets (i.e., building masks) have been
utilized to construct and train the state-of-the-art deep Fully Convolution
Neural Networks with an additional Conditional Random Field represented as a
Recurrent Neural Network to detect building regions in a single VHR SAR image.
Such a cascaded formation has been successfully employed in computer vision and
remote sensing fields for optical image classification but, to our knowledge,
has not been applied to SAR images. The results of the building detection are
illustrated and validated over a TerraSAR-X VHR spotlight SAR image covering
approximately 39 km almost the whole city of Berlin with mean
pixel accuracies of around 93.84%Comment: Accepted publication in IEEE TGR
Single-Look SAR Tomography of Urban Areas
Synthetic aperture radar (SAR) tomography (TomoSAR) is a multibaseline interferometric technique that estimates the power spectrum pattern (PSP) along the perpendicular to the line-ofsight (PLOS) direction. TomoSAR achieves the separation of individual scatterers in layover areas,
allowing for the 3D representation of urban zones. These scenes are typically characterized by buildings of different heights, with layover between the facades of the higher structures, the rooftop of the smaller edifices and the ground surface. Multilooking, as required by most spectral estimation techniques, reduces the azimuth-range spatial resolution, since it is accomplished through the averaging of adjacent values, e.g., via Boxcar filtering. Consequently, with the aim of avoiding the spatial mixture of sources due to multilooking, this article proposes a novel methodology to perform single-look TomoSAR over urban areas. First, a robust version of Capon is applied to focus the TomoSAR data, being robust against the rank-deficiencies of the data covariance matrices. Afterward, the recovered PSP is refined using statistical regularization, attaining resolution enhancement, suppression of artifacts and reduction of the ambiguity levels. The capabilities of the proposed methodology are demonstrated by means of strip-map airborne data of the Jet Propulsion
Laboratory (JPL) and the National Aeronautics and Space Administration (NASA), acquired by the uninhabited aerial vehicle SAR (UAVSAR) system over the urban area of Munich, Germany in 2015. Making use of multipolarization data [horizontal/horizontal (HH), horizontal/vertical (HV) and vertical/vertical (VV)], a comparative analysis against popular focusing techniques for urban monitoring (i.e., matched filtering, Capon and compressive sensing (CS)) is addressed
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