The new spaceborne very high resolution (VHR) synthetic aperture radar (SAR) sensors, such as TerraSAR-X
and Cosmo-SkyMed permit to extract information from VHR SAR data in urban areas at the level of individual
buildings. To support the widespread usage of VHR SAR for different application scenarios (e.g. damage
assessment after natural disasters), and hence to increase the value of the data, automatic methods for building
detection and reconstruction should be developed. In this paper we present a novel method for automatic
building detection from single detected VHR SAR imagery. In a first step we extract basic features (e.g. lines)
and identify their semantic meaning modeled as the degree of membership of the feature to a certain scattering
class (e.g. double bounce). Then, we analyze the features extracted in a neighborhood system to identify building
candidates using a production system. Finally, a score is calculated for each building candidate based on the
semantic meaning of its features. This score is used to select or reject a candidate for the final set of extracted
buildings. The preliminary results obtained on single detected VHR SAR images of an urban area show that the
method effectively detects flat and gable roof buildings with only few missed and almost no false alarms.
High Performance Computing (HPC) hardware solutions such as grid computing and General Processing on a Graphics
Processing Unit (GPGPU) are now accessible to users with general computing needs. Grid computing infrastructures in
the form of computing clusters or blades are becoming common place and GPGPU solutions that leverage the processing
power of the video card are quickly being integrated into personal workstations. Our interest in these HPC technologies
stems from the need to produce near real-time maps from a combination of pre- and post-event satellite imagery in
support of post-disaster management. Faster processing provides a twofold gain in this situation: 1. critical information
can be provided faster and 2. more elaborate automated processing can be performed prior to providing the critical
information. In our particular case, we test the use of the PANTEX index which is based on analysis of image textural
measures extracted using anisotropic, rotation-invariant GLCM statistics. The use of this index, applied in a moving
window, has been shown to successfully identify built-up areas in remotely sensed imagery. Built-up index image masks
are important input to the structuring of damage assessment interpretation because they help optimise the workload. The
performance of computing the PANTEX workflow is compared on two different HPC hardware architectures: (1) a blade
server with 4 blades, each having dual quad-core CPUs and (2) a CUDA enabled GPU workstation. The reference
platform is a dual CPU-quad core workstation and the PANTEX workflow total computing time is measured.
Furthermore, as part of a qualitative evaluation, the differences in setting up and configuring various hardware solutions
and the related software coding effort is presented.
This paper presents a detailed experimental study on the behavior of the backscattering of buildings in very high resolution (VHR) synthetic aperture radar (SAR) images under varying conditions. The double bounce effect caused by the corner reflector between the front wall of the buildign and its surrounding ground area is an important characterist of the building in VHR SAR. Therefore, we focus on the analysis of the relation between the double bounce effect and the aspect angle of the building. The study is carried out in three phases: i)development of a laboratory experimental setup on a scaled building model under well-controlled conditions with a variety of viewing configurations; ii) validation of the results obtained from the laboratyr measurements with real VHR airborne SAR data; iii) comparison of the above mentioned results with the simulations obtained by two theoretical models derived from electromagnetic theory. The laboratory experiments were carried out at the European Microwave Signature Lab (EMSL) at the Joint Research Centre (JRC) of the European Commission (EC), whie the real airborne SAR images wer acquired by the RAMSES sensor and were processed in order to obtain simulations of COSMO-SkyMed satellite images. The analyses showed that the strength of the double bounce drops rapidly in the low aspect angle range, while it decreases moderately for larter angles.
The new spaceborne very high resolution (VHR) synthetic aperture radar (SAR) sensors onboard the TerraSARX
and COSMO-SkyMED satellites have a spatial resolution of up to 1 meter. In VHR SAR data, features from
individual urban structures (like buildings) can be identified in their characteristic settings in urban settlement
patterns. In this paper, we present a novel methodology for the height estimation for generic man made structures
from single power SAR data. The proposed approach is based on the definition of a hypothesis on the height
of the building and on the simulation of a SAR image for testing that hypothesis. Then a matching procedure
is applied between the estimated and the actual SAR images in order to validate the height assumption. The
process is iterated for different initial height assumptions until the matching function is satisfied and thus the
building height is estimated. The efficiency and the properties of the proposed method are demonstrated for the
height estimation of a set of 38 flat- and gable roof buildings using a VHR airborne SAR scene for a residential
area in Dorsten, Germany.