Nicolas Trouve

The French Aeropace Lab ONERA, Paliseau, Île-de-France, France

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Publications (8)7.03 Total impact

  • Elise Colin-Koeniguer · Nicolas Trouve
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    ABSTRACT: This paper investigates the use of polarimetry to improve the estimation of the height of buildings in high-resolution synthetic aperture radar (SAR) images. Polarimetric coherence optimization techniques solve the problem of layover effects in urban scenes by allowing a phase separation of the scatterers sharing the same resolution cell. Bare soil elevation estimation is also improved by the polarimetric phase diversity. First, we present an analysis of the statistical modeling of the generalized coherence set. A building height estimation method is then derived from this analysis. Finally, the method is tested and quantitatively validated over an X-band polarimetric interferometric SAR (PolInSAR) airborne image acquired in a single-pass mode, containing a set of 140 different buildings with ground truth.
    IEEE Transactions on Geoscience and Remote Sensing 09/2014; 52(9):5870-5879. DOI:10.1109/TGRS.2013.2293605 · 3.51 Impact Factor
  • Nicolas Trouve · Maxime Sangnier · Elise Colin Koeniguer
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    ABSTRACT: In the field of polarimetric, interferometric, or multivariate SAR image segmentation or classification, statistical models are used to design equality test or similarity measure. In this work we address the impact of the choice of various non gaussian models on the final performance in classification or segmentation of urban polarimetric SAR images. As more parameters are used in the description of the distribution, more estimation errors are introduced. It yields that the most complex distribution performance can often be outmatched by simpler models, even in very high resolution or urban settings. As the clutter distribution evolves from gaussian noise to an impulsive non gaussian distribution, there is a breaking point at which using a non gaussian model really becomes beneficial performance wise. In this paper we present preliminary results and methods to improve the choice of an appropriate signal model for a given polarimetric SAR image.
    Synthetic Aperture Radar, 2012. EUSAR. 9th European Conference on; 01/2012
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    ABSTRACT: This paper deals with the behavior of the depolarization of natural environments in bistatic settings. In bistatic radar imaging, we seek simulation tools capable of predicting this depolarization for any geometric configuration. As we lack actual real data to validate such tools, we propose an alternative measurement method, at the optical scale, on depolarizing media consisting of carbon nanotubes. We present the first results of such measures, and we offer a number of phenomenological interpretations using also a simulation tool.
    Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International; 01/2012
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    ABSTRACT: Within the frame of bistatic polarimetry, this paper discusses the entangled effects of bistatic geometry and target features on polarimetric measurements. Three different geometrical effects are distinguished: antenna rotations, target orientation, and bistatic angle. Antenna rotations are addressed through the use of polarimetric bases taking the scattering plane as the reference plane. Target orientation effects are not considered since only spheres are studied. This paper focuses on the bistatic angle effect through a bistatic polarimetric analysis on classical parameters. Targets consisting of single or multiple spheres in the resonance region are investigated. Finally, the results of indoor polarimetric measurements on such targets are presented and discussed.
    IEEE Transactions on Geoscience and Remote Sensing 07/2011; DOI:10.1109/TGRS.2010.2093533 · 3.51 Impact Factor
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    POLINSAR Workshop, 2011; 01/2011
  • N. Trouve · E. Colin-Koeniguer
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    ABSTRACT: Many texture models have been studied and applied to classification and segmentation of Polarimetric synthetic aperture radar (PolSAR) data. The Gamma texture distribution leads to the classical K distributed covariance matrix, and more recently the Fisher distribution has been studied in "Bombrun, Beaulieu, Fisher Distribution for Texture Modeling of Polarimetric SAR Data" and leads to the KummerU distribution. Good results have been demonstrated using more accurate texture distribution, increasing the number of parameters used to describe the distributions. But those parameters need a large amount of samples to be estimated, increased computing time, and when used in segmentation algorithm the models cannot be used until large segments have been delineated. The segmentation is then usually very sensitive to the first steps and require strong shape constraints. The SIRV model (Spherically Invariant Random Vectors) based on the compound Gaussian model includes most texture distributions and does not require estimation of the texture parameters. That model is very convenient for PolSAR segmentation as it is much more accurate than the Gaussian model, and allows us to use the full resolution available while maintaining reduced computing times. In this paper we propose to apply the recent results on SIRV models and study their application on the segmentation of high resolution PolSAR or PolInSAR data. A SIRV based distance, adapted to any kind of texture, is proposed. Advantages are discussed and results on RAMSES PolSAR images at X-band are provided.
    Synthetic Aperture Radar (EUSAR), 2010 8th European Conference on; 07/2010
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    Nicolas TROUVE · Elise COLIN
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    ABSTRACT: Modelling of a cloud of cylinders can be interesting within the framework of SAR images of natural media: forest, crops, and so on. Similar types of models and descriptions are used in optics and light scattering for optical media such as skin or colloidal solutions. In this paper, several polarimetric parameters are investigated on this type of targets. These parameters are dedicated to the study of natural media, such as the ones obtained by the Cloude-Pottier decomposition, or given by the Lu and Chipman decomposition which is commonly used in optical polarimetry. The flrst one has been developed for the monostatic case, whereas the second one is more dedicated to forward scattering. In order to be able to understand and to compare them, these both decompositions are tested for the range of bistatic angles varying from 0 (backscattering case) to …(forward scattering case). In order to control the parameters of the target, we have proposed a simple geometrical model of a cloud of cylinders with a given orientation distribution. This approach is validated both comparing it to the flelds modelled by COSMO (inflnite cylinder approximation) and by a comparison to the indoor measurement of a group of cylinders. We are thus able to study the variation of bistatic polarimetric parameters. Among others, it is pointed out that the Lu and Chipman decomposition is correct for the forward direction (bistatic angle higher than … 2) but need to be adapted for bac kscattering conflgu- ration (or bistatic angle lower than … 2. Di-culties in the interpretation of alpha in the bistatic context are also discussed. ferent attenuations on the eigenpolarizations), and the random volume model is commonly used to describe natural media (forest in radar, skin cells in optics, etc.) In this paper, we propose a simple modelling of a cloud of cylinders with a given distribution of orienta- tions. To achieve the maximum of literal calculations, we chose a truncated uniform angular distribution. For simplicity raisons, we consider the case of the normal incidence, and of an azimuthal bistatic angle, varying from the monostatic conflguration to the forward scat- tering conflguration. The flrst case is well known in radar, whereas the second case is frequently met in optics. This conflguration allows to study how the var- ious polarimetric parameters evolve continuously from backscattering to forward scattering. This geometry is represented on flgure 1.
    POLINSAR; 01/2009
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    Olivier Rabaste · Nicolas Trouvé
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    ABSTRACT: This paper addresses the detection problem in moderately non-Gaussian environments. We first propose an analysis of the classical Gaussian and SIRV detectors from a new point of view. We define a robustness criteria with respect to signal mismatch and we demonstrate that the non-Gaussian detec-tor is robust only for small mismatches. We then propose a new family of detectors based on a geometric heuristic that exploits advantages of both Gaussian and non-Gaussian de-tectors. This new family is robust to signal mismatch and presents very interesting detection performance.