René Garello

On rewriting the imaging mechanism of underwater bottom topography by synthetic aperture radar as a Volterra series expansion

Ocean surface current gradients can be imaged by real and synthetic aperture radar (RAR/SAR) due to the so-called hydrodynamic modulation mechanism. This is the reason why underwater bottom topography and internal waves are visible on radar images. Several physical models exist for this imaging mechanism. When trying to obtain current information from SAR images, a problem arises: the imaging mechanism can be nonlinear. We propose to rewrite the classical modeling of the SAR underwater bottom topography imaging mechanism by using a Volterra series expansion. The Volterra model can be seen as a tool allowing us to study whether the imaging mechanism can be inverted or not. It is a transposition of a well-studied physical problem into a more explicit expression. The conditions for the inversion of the Volterra model are presented and a scheme for estimating the bathymetry from SAR images is described. The main property of the inversion algorithm is its independence from the physical model used for the mechanism.

Parametrisation of sea state from SAR images

An attempt is made to characterize the sea state as it is seen on synthetic aperture radar (SAR) images. To do so, a spectral analysis of the images is performed in order to extract relevant parameters of the sea swell, such as direction of propagation, orientation, and strength. Then a punctual statistical analysis of the surface roughness allows the completion of the previous set of parameters. An images gray-level distribution is computed assuming some hypothesis on the sea surface backscattering. It is shown, using the Bragg scattering model, that the shape parameter alpha of the derived K law is characteristic of the sea surface roughness. Using SEASAT SAR images, the discrimination nature of the aforementioned set of parameters is highlighted for some selected sea states.<<ETX>>

Unsupervised Classification of Radar Images Based on Hidden Markov Models and Generalised Mixture Estimation

Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This article describes unsupervised classification of radar images in the framework of hidden Markov models and generalised mixture estimation. In particular, we show that hidden Markov chains, based on a Hilbert-Peano scan of the radar image, are a fast and efficient alternative to hidden Markov random fields for parameter estimation and unsupervised classification. We also describe how the distribution families and parameters of classes with homogeneous or textured radar reflectivity can be determined through generalised mixture estimation. Sample results obtained on real and simulated radar images are presented.

3D extraction from airborne SAR imagery

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.