Sylvie Le Hégarat-Mascle

Surface and aerodynamic roughness in arid and semiarid areas and their relation to radar backscatter coefficient

Surface roughness is a key parameter for surface-atmosphere exchanges of mass andenergy. Only a few field measurements have been performed in arid or semiarid areaswhere it is an important control of the aeolian erosion threshold. An intensive fieldcampaign was performed in southern Tunisia to measure the lateral cover, Lc, and theaerodynamic roughness length, Z0, over 10 sites with different surface roughnesses. Lcwas determined by combining field measurements of the geometry of the roughnesselements and simple assumptions on their shapes. Z0was experimentally determined fromhigh-precision wind velocity and air temperature profiles. The resulting data were found tobe in good agreement with the existing relationships linking the geometric and theaerodynamic roughness. This suggests that for natural surfaces, Z0can be estimated onthe basis of the geometric characteristics of the roughness elements. This data set wasthen used to investigate the capabilities of radar backscatter coefficients, s0, to retrieve Lcand/or Z0. Significant relationships were found between s0and both Lcand Z0. TheSAR/ERS data set is in agreement with the SIR-C SLR data set from Greeley et al. (1997).On the basis of these two data sets including data from different arid and semiarid areas(North Africa, South Africa, North America), we propose an empirical relationship toretrieve Z0using radar observations in the C band from operational sensors.

Application of ant colony optimization to adaptive routing in aleo telecomunications satellite network

Ant colony optimization (Aco) has been proposed as a promising tool for adaptive routing in telecommunications networks. The algorithm is applied here to a simulation of a satellite telecommunications network with 72Leo nodes and 121 earth stations. Three variants ofAco are tested in order to assess the relative importance of the different components of the algorithm. The bestAco variant consistently gives performance superior to that obtained with a standard link state algorithm (Spf), under a variety of traffic conditions, and at negligible cost in terms of routing bandwidth.RésuméUne méthode d’optimisation utilisant des agents de type“fourmis”(ant colony optimi zation) est proposée pour les problèmes de routage dynamique dans les réseaux de télécommunications. L’algorithme est appliqué à un réseau de satellites comprenant 72 satellites leo et 121 stations terriennes. Trois versions de Valgorithme sont comparées dans le but d’évaluer l’importance relative des différentes composantes de l’algorithme. La version complète de l’algorithme donne de façon systématique des résultats meilleurs que ceux obtenus par Valgorithme standard spf, ceci pour différentes conditions de trafic, et un coût moindre en termes de bande passante.

Application of Shannon information theory to a comparison between L- and C-band SIR-C polarimetric data versus incidence angle

Abstract The aim of this article is to present a quantitative measurement of the redundancy, or mutual information, between two different images of the same site. It is based on Shannon and Wiener information theory. In the case of polarimetric synthetic aperture radar images, we propose to compute redundancy either at the radiometric level, according to the error bar on measurement, or at the class level, according to supervised or unsupervised classification results. The advantage of the comparison at the class level is that both Polarimetric information and spatial neighborhood information are considered, and therefore the comparison is performed at a higher level of information. This measurement has been applied to the study of redundancy between L and C bands from spaceborne imaging radar C images of the Orgeval test site, located in the cast of Paris (France), versus incidence angle. It is shown that the redundancy between the two bands increases from about 20% to 30% when incidence angle increases from 44° to 57°. Based on the analysis of classification results in terms of redundancy with ground truth, an interpretation of this result is proposed. Finally, the great complementarity between the two bands is used to improve classification results, by performing data fusion between the two bands.

A-Contrario Modeling for Robust Localization Using Raw GNSS Data

In Global Navigation Satellite System (GNSS) positioning, urban environments represent an issue particularly because of multipath and nonline-of-sight effects. The latter effects induce erroneous pseudorange observations that then should be discarded in order not to affect the estimation of the receiver position. This paper proposes a new approach for the detection of outliers in the pseudorange observations. Based on two models representing the distribution of inconsistent data (naive models), two criteria are proposed to partition the data between inliers and outliers and to estimate the location parameters. These criteria are then implemented in two localization algorithms. In addition, by considering hypotheses specific to GNSS localization, pseudorange selection and a regularization step are implemented in order to reduce the complexity and to improve the problem conditioning. Using simulated and actual datasets, the proposed algorithms are compared with popular and recent methods addressing the GNSS positioning problem. We show that the outlier detection improves the estimation of the receiver location and outperforms the classical approaches particularly when the environment is constrained.

Evidential framework for data fusion in a multi-sensor surveillance system

The multi-sensor data fusion relies on a combination of information pieces to produce a more accurate or complete description of the environment. In this work, we considered the case of a surveillance system using several heterogeneous sensors in a network. In such a system, the data fusion objective is to merge the detections provided by the different sensors in order to count, locate and track all the targets in the monitored area. The problem was addressed in the context of Belief Function theory in order to cope with the high inaccuracy of information and the different forms of imprecision. In this framework, we developed a unified approach to model and merge the detections coming from various kinds of sensors with prior knowledge about target location derived from topographical elements. We showed that the developed belief model provided an efficient measurement for data association between tracks and detections. Considering scalable constraints for the system, the complexity and consistency of belief function representation should be controlled, which was achieved by implementing versatile discernment frames and by restricting the number of focal elements. The proof of concept of the proposed data fusion module was achieved by implementing it in an actual detection system. Real-world scenarios were used to draw some conclusions about localization performance and end-user perception. Further experiments were also performed on simulated data to focus on data association and belief function simplification subproblems.

Outlier Detection in GNSS Pseudo-Range/Doppler Measurements for Robust Localization.

In urban areas or space-constrained environments with obstacles, vehicle localization using Global Navigation Satellite System (GNSS) data is hindered by Non-Line Of Sight (NLOS) and multipath receptions. These phenomena induce faulty data that disrupt the precise localization of the GNSS receiver. In this study, we detect the outliers among the observations, Pseudo-Range (PR) and/or Doppler measurements, and we evaluate how discarding them improves the localization. We specify a contrario modeling for GNSS raw data to derive an algorithm that partitions the dataset between inliers and outliers. Then, only the inlier data are considered in the localization process performed either through a classical Particle Filter (PF) or a Rao-Blackwellization (RB) approach. Both localization algorithms exclusively use GNSS data, but they differ by the way Doppler measurements are processed. An experiment has been performed with a GPS receiver aboard a vehicle. Results show that the proposed algorithms are able to detect the ‘outliers’ in the raw data while being robust to non-Gaussian noise and to intermittent satellite blockage. We compare the performance results achieved either estimating only PR outliers or estimating both PR and Doppler outliers. The best localization is achieved using the RB approach coupled with PR-Doppler outlier estimation.

Robust crack detection for unmanned aerial vehicles inspection in an a-contrario decision framework

Abstract. We are interested in the performance of currently available algorithms for the detection of cracks in the specific context of aerial inspection, which is characterized by image quality degradation. We focus on two widely used families of algorithms based on minimal cost path analysis and on image percolation, and we highlight their limitations in this context. Furthermore, we propose an improved strategy based on a-contrario modeling which is able to withstand significant motion blur due to the absence of various thresholds which are usually required in order to cope with varying crack appearances and with varying levels of degradation. The experiments are performed on real image datasets to which we applied complex blur, and the results show that the proposed strategy is effective, while other methods which perform well on good quality data experience significant difficulties with degraded images.

A New Local Measure of Disagreement between Belief Functions – Application to Localization

In the theory of belief functions, the disagreement between sources is often measured in terms of conflict or dissimilarity. These measures are global to the sources, and provide few information about the origin of the disagreement. We propose in this paper a “finer” measure based on the decomposition of the global measure of conflict (or distance). It allows focusing the measure on some hypotheses of interest (namely the ones likely to be chosen after fusion).We apply the proposed so called “local” measures of conflict and distance to the choice of sources for vehicle localization.We show that considering sources agreement/disagreement outperforms blind fusion.

SVM Classifier Fusion Using Belief Functions: Application to Hyperspectral Data Classification

Hyperspectral imagery is a powerful source of information for recognition problems in a variety of fields. However, the resulting data volume is a challenge for classification methods especially considering industrial context requirements. Support Vector Machines (SVMs), commonly used classifiers for hyperspectral data, are originally suited for binary problems. Basing our study on [12] bbas allocation for binary classifiers, we investigate different strategies to combine two-class SVMs and tackle the multiclass problem. We evaluate the use of belief functions regarding the matter of SVM fusion with hyperspectral data for a waste sorting industrial application. We specifically highlight two possible ways of building a fast multi-class classifier using the belief functions framework that takes into account the process uncertainties and can use different information sources such as complementary spectra features.

Segmentation of elevation images based on a morphology approach for agricultural clod detection

This study deals with the segmentation of altitude or elevation images, i.e. images of the distance (z-coordinate) between the surface or objects and the camera plane. Specifically to our soil science application, these images are acquired on agricultural surfaces in order to evaluate their roughness. The cloddy structure being a key factor to characterize soil roughness, the elevation image analysis aims at detecting and identifying the clods as accurately as possible. Now, rather than defining a new segmentation algorithm, we propose to transform the input data so as to take into account the different criteria characterizing the clod objects, namely the relative altitude and a function of the gradient norm. The proposed approach was validated on three agricultural surfaces (two synthetic and one real) and the results compared to those of an algorithm previously developed specifically for the clod identification problem.