J.-M. Le Caillec

Multiscale oil slick segmentation with Markov chain model

A Markov chain model is applied for the segmentation of oil slicks acquired by SAR sensors. Actually, oil slicks have specific impact on ocean wave spectra. Initial wave spectra may be characterized by three kinds of waves, big, medium and small, which correspond physically to gravity and gravity-capillary waves. The increase of viscosity due to the presence of oil damps gravity-capillary waves. This induces a damping of the backscattering to the sensor, but also a dampening of the energy of the wave spectra. Thus, local segmentation of wave spectra may be achieved by the segmentation of a multiscale decomposition of the original SAR image. In this work, the unsupervised segmentation is achieved by using a vectorial extension of the Hidden Markov Chain (HMC) model. Parameters estimation is performed using the general Iterative Conditional Estimation (ICE) method. The problem of estimating multi-dimensional and non-Gaussian densities is solved by using a Principal Component Analysis (PCA). The algorithm has been applied on an ERS-PRI image. It yields interesting segmentation results with a very limited number of false alarms. Also, the multiscale segmentation proved to be an interesting alternative to classify marginal or degraded slicks.

Estimation of sea-ice SAR clutter statistics from Pearson's system of distributions

SAR images can be used to help ship routing in sea-ice conditions. In this study, we focus on the Antarctic region where no multi-year ice nor big ice floes are to be found. As a matter of fact, each clutter obeys a backscattering mechanism that induces a specific pixel distribution and our attempt is to identify automatically the correct distribution for each ice type. The problem is that of generalized mixture estimation and unsupervised image classification. In this work, we modelled the mixture with distributions from Pearsons system. Parameter estimation is realized according to the ICE algorithm in the context of hidden Markov chains. The results obtained from Pearsons system are compared to ones obtained with a classical mixture of Gaussian distributions.

Radargrammetric processing for 3-D building extraction from high-resolution airborne SAR data

This paper deals with 3-D extraction of buildings from stereoscopic high-resolution SAR images. Data have been acquired with the airborne RAMSES sensor of ONERA. In our context, roofs are not very textured and shadows are not visible, whereas strong L-shaped echoes on rectangular buildings appear. We apply an adapted radargrammetric chain leading to the DEM (Digital Elevation Model) from two stereoscopic images. Results from two stereoscopic pairs are compared to an accurate 3-D model. It is shown that this radargrammetric processing leads to pertinent 3-D information except for structures with no significant signature.

Space-Time Adaptive Processing analysis for the moving target on the sea surface indication purpose

Space-time adaptive processing (STAP) can improve target detectability in a presence of a ground clutter for airborne radar. Ground clutter echo has a wide spectrum as a result of the radar platform (airplane or satellite) motion. To reject clutter echo and preserve target echo, STAP employs antenna array. Simultaneous filtering in both spatial (angle) and frequency domain can improve performance. In this paper we propose a new application of STAP. Assuming an antenna array standing on a sea shore, the objective is to detect targets on the sea surface. Unfortunately sea clutter has different statistical properties compared to airborne clutter. As a consequence, basic STAP algorithm is not optimal in any sense. Future research will face the problem of evaluating performance of STAP applied to sea clutter. We hope to develop a new algorithm that can perform better in the presence of sea clutter.

Detection of nonlinearity in sea surface SAR imaging process using bispectrum method estimation

Higher order moments have been, for the last decade, an important field of interest, but generally limited to one dimensional signal cases. We introduce in this paper a 2D bispectrum to detect nonlinearity in the SAR image mapping process, by using the bicoherency of a 2D signal which is theoretically flat over all frequencies, if the process is linear. Two bicoherency estimators are developed, the first one using a direct method for bispectral estimation and a periodogram for the spectral estimator, and the second one based on an indirect method and correlogram. In order to validate our nonlinearity detection method, we have tested it first on two simulated images, a linear one and a nonlinear one, and then on two SAR images, one seeming to be nonlinear. Conclusions are drawn by comparing the SAR image and the simulated image bicoherencies.

Effect of integration time on detection performance of MTI and STAP algorithms in HF radar

Surface wave over the horizon radar systems have a potential to detect targets falling far beyond optical horizon. Unfortunately, first and second order clutter scattered from the ocean surface, in combination with interferences, can prevent successful deployment of such systems. In this paper we compare different adaptive MTI and space-time adaptive processing algorithms applied to the surface wave high frequency radar system data. The aim of the experiments was to detect small targets (20-30 m long) at distances about 50 km. Radar system used in the experiments is an oceanographic, surface-wave, high-frequency system WERA, designed to perform continuous monitoring of sea currents and waves. System used in this research is located at the east coast of France, in Brittany. Main task of the system is to provide continuous, real-time information about currents and waves in the English (La Manche) Channel entry area, where heavy ship traffic is present.

Study of the second order approximation of the velocity bunching in the SAR imaging process using the bispectrum

The authors comment upon some simulations of the SAR imaging process obtained by using, firstly the complete nonlinear transform (i.e. the velocity bunching), and secondly its second order approximation (i.e. a second order Volterra model), in order to study the quadratic nonlinear interaction strength and localization and to compare with results obtained on ERS-1 images.

Practical problems with covariance matrix estimation for adaptive MTI and space-time adaptive processing for target detection in HF surface wave radars

Surface wave over the horizon radar systems have a potential to detect targets, that are hidden under optical horizon. This possibility is, however, difficult to implement as a result of first and second order clutter scattered from the ocean surface in combination with interferences. In this paper we compare adaptive MTI and space-time adaptive processing algorithms applied to the surface wave high frequency radar system data. The aim of the experiments was to evaluate different techniques and their usefulness to detect targets at distances 50-200 km off shore. In the experiments authors used the data from an oceanographic, surface-wave, high frequency system WERA, designed to perform continuous monitoring of sea currents and waves. Data were collected at the west coast of France, in Brittany.

Short wave spectrum modulation by a surface current field and long waves for SAR imaging process simulation

In the framework of the SAR imaging process simulator we are developing, the modification of the short wave spectrum by ocean surface is of great importance. Short waves at the sea surface produce the backscattering of the radar wave. So the modulation of the short wave distribution by ocean surface induces the modulation of the SAR image. In this paper we present a two-step algorithm designed to simulate the modulation of the short waves by both current and long waves. First of all the modulation of the long waves by current is taken into account. Then the modification of the short wave spectrum by long-wave orbital velocity and current is considered. To achieve the calculation we use the action balance equation (ABE) based on a weak interaction theory. ABE theory and the two-step algorithm are described here

A new ocean SAR imaging process simulator

In this paper, we develop the concept of a new ocean SAR imaging process simulator. We intend to come up with a simulator more complete than those which have been developed until now. Indeed, in addition to the large number of oceanic and atmospheric phenomena considered, the simulator should lead to cope with various radar configurations (spaceborne, airborne, grazing-angle). The simulator is based on several interconnected units allowing to model the sea surface features and their interactions with the radar electromagnetic waves. We develop the hydrodynamic modulation of the short waves by the long waves which are at the core of the process. The peculiarities of different type of radars and radar carriers are also included.