For several years, image classification and pattern recognition algorithms have been developed for the land coverage mapping using radar and multispectral imagery with medium to large pixel size. As several satellites now distribute submetric-pixel and metric-pixel images (for example QUICKBIRD,TERRASAR-X), the research turns to the study of the structure of cities: building structuring, grassy areas, road networks, etc, and the physical description of the urban surfaces. In that context, we propose to underline new potentialities of submetric-pixel polarimetric SAR images. We deal with the characterization of roofs and the mapping of trees. For that purpose, a first analysis based on photo-interpretation and the assessement of several polarimetric descriptors is carried out. Then, an image classification scheme is built using the polarimetric H/alpha-Wishart algorithm, followed by a decision tree. This one is based on the most pertinent polarimetric descriptors and aims at reducing the classification errors. The result proves the potential of such data. Our work relies on an image of a suburban area, acquired by the airborne RAMSES SAR sensor of ONERA.
Many works and European projects have proven the ability of Permanent Scatterers Synthetic Aperture Radar
Interferometry (PS-InSAR) to measure the slow deformation of the persistent ground objects with a millimetric precision
measurement. Compared to the classical differential SAR Interferometry (DInSAR), PS-InSAR is an approach that
estimates several contributions: atmospheric disturbances, orbital errors, deformation signal as well as topographical
In this paper, we propose to apply PS-InSAR for the analysis of the ground deformation phenomena in a urban context.
For that purpose, the Stanford Method for Permanent Scatterers (StaMPS) is applied using an ERS data archive. StaMPS
was developed in Stanford University by Andy Hooper. The advantages to use StaMPS were that it is free and many
scripts are already available to process the dataset. In first steps of the processing, differential interferograms are
produced using the Delft Object-oriented Radar Interferometric Software (DORIS), developed in Delft University of
Technology . Doris is also a free tool.
StaMPS method is briefly explained. A first experiment on the city of Paris, France, is presented, especially because PSINSAR
and DInSAR results have already been published by several researchers. Therefore, the processing of Nantes
(French city) is carried out. Some important results are shown.
Nowadays, several free or commercial tools for INSAR, DINSAR and even PS INSAR exist. A brief inventory of
current suites is drawn and shows their main capabilities. The objective of this work is the handling and the validation of
our use of DORIS, a free processing suite. For this purpose, the organization of the processing is studied and compared
to another one (SARSCAPE, a commercial software). It appears that both chains contain identical or similar steps but,
some stages are missing or are replaced by a different one. Experiments are then held with DORIS and SARSCAPE
using the same data set: two ASAR images covering the 2003 Bam (Iran) earthquake and a SRTM3 DEM. Using DORIS
and SARSCAPE, it leads to comparable results, which allows validating our use of DORIS (and also SARSCAPE).
However, linear and non linear residual differences occur. In the same way, dissimilarities are observed with other
published results. These dispecrancies are interpreted as resulting from residual geometric inaccuracies. One solution to
reduce these artefacts is a simple surface compensation but the orbital parameters are then not corrected. Finally, this
work may give some help for a better understanding of any DINSAR processing chain.
This paper deals with automatic extraction of 3-D buildings from stereoscopic high-resolution images recorded by the RAMSES sensor. Roofs are not very textured whereas typical strong L-shaped echoes are visible. These returns generally result from dihedral corners between ground and structures. They provide a part of the building footprints and the ground altitude, but not building heights. Thus, we present an adapted process including two main steps: - stereoscopic structure extraction from L-shaped echoes. Buildings are detected on each image using the Hough transform. Then they are recognised during a stereoscopic refinement stage based on a criterion minimisation. - height measurement. As most of previous extracted footprints indicate the ground altitude, building heights are found by monoscopic and stereoscopic measures. Between structures, ground altitudes are obtained by a dense matching process. This processing is applied on an industrial scene. Results are compared with a ground truth. Advantages and limitations of the method are brought out.
It is well known that a SAR image is composed of two types of information: amplitude and phase. Nevertheless, the information contained in the phase is hardly exploited on its own. Indeed, the number of processes at work and the scale difference between the image resolution and the wavelength induce, with regard to the phase, a quasi-random spatial behavior. However, our recent work shows that the phase of one image can be spatially correlated. First, we define an estimator for the spatial correlation of the phase, and study its behavior with real data. We assess the phase correlation according to the resolution and the type of surface. Then, we set down the theoretical bases of a statistical model of this behavior. We highlight the conditions required with regard to the resolution, the sampling rate, and the impulse response. We therefore identify the best kinds of surfaces, so that the phenomenon occurs. Hence, we simulate the phase correlation for different cases according to the phase model defined. We choose suitable parameters to the conditions of the real data and compare measurements and simulations. Finally, we propose possible applications related to the use of this new source of information.
In the state-of-the-art of 3D extraction from SAR images, we can distinguish three main techniques: radarclinometry, interferometry and radargrammetry. Our project is to perform radargrammetry on high-resolution images recorded by the airborne sensor RAMSES designed and operated by ONERA. Such images allow the visualization of infrastructures and urban areas. First, we are interested in the geometric stereomodel and the location errors due to sensor parameter and disparity errors. We propose a rigorous theoretical error model for every viewing configuration. We use it in order to study the geometric potential of RAMSES sensor for three configurations. We are then dealing with the pertinence of strong reflectors. Their study is based on the analysis of the 3D information extracted from matched points imaged as strong reflectors. For that purpose, we use RAMSES stereo images of an industrial site. Because of many metallic components, the image of this site presents a large quantity of strong echoes that makes difficult the visual interpretation of the scene. We show the first quantitative results on the potentiality of high- resolution radargrammetry on strong reflectors. This analysis allows us to conclude on the possibility of radargrammetric applications with high-resolution airborne sensors.