The ONERA RAMSES SAR: Latest Significant Results and Future Developments

Dreuillet, Philippe; Cantalloube, Hubert; Colin-Koeniguer, Élise; Dubois-Fernandezx, P.; Dupuis, Xavier; Fromage, P.; Garestier, Franck; Heuze, D.; Oriot, Hélène; Peron, Janusz; Peyret, J.; Bonin, Gregory; Plessis, Olivier Ruault du; Nouvel, Jean-Francois; Vaizan, Bernard

doi:10.1109/radar.2006.1631849

Cited by 21 publications

(7 citation statements)

References 7 publications

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“…Data is from the ONERA airborne RAMSES (Radar Aéroporté Multi-Spectral d'Etude des Signatures) SAR [9,10] and is delivered in SLC (Single Look Complex) format, acquired in 2006 with an incidence angle of 60° and a sub-metric pixel size in both azimuthal and range directions. These sampling sizes allow observing lots of urban objects (large and small buildings, natural and artificial soils).…”

Section: Sar Datamentioning

confidence: 99%

Maximum Likelihood Classification of High-Resolution Sar Images in Urban Area

Majd¹,

Simonetto²,

Praly³

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this work, we propose a state-of-the-art on statistical analysis of polarimetric synthetic aperture radar (SAR) data, through the modeling of several indices. We concentrate on eight ground classes which have been carried out from amplitudes, co-polarisation ratio, depolarization ratios, and other polarimetric descriptors. To study their different statistical behaviours, we consider Gauss, lognormal, Beta I, Weibull, Gamma, and Fisher statistical models and estimate their parameters using three methods: method of moments (MoM), maximum-likelihood (ML) methodology, and log-cumulants method (MoML). Then, we study the opportunity of introducing this information in an adapted supervised classification scheme based on Maximum-Likelihood and Fisher pdf. Our work relies on an image of a suburban area, acquired by the airborne RAMSES SAR sensor of ONERA. The results prove the potential of such data to discriminate urban surfaces and show the usefulness of adapting any classical classification algorithm however classification maps present a persistant class confusion between flat gravelled or concrete roofs and trees.

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Section: Sar Datamentioning

confidence: 99%

Maximum Likelihood Classification of High-Resolution Sar Images in Urban Area

Majd¹,

Simonetto²,

Praly³

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…To illustrate the improvements in the standard POLSAR processing chain, results obtained with high and very high resolution airborne data are reported. Both data sets were acquired by the airborne ONERA RAMSES system [51]. • BN-SCM: "patchy" appearance mainly due to the texture;…”

Section: B Airborne Polsar Datamentioning

confidence: 99%

Coherency Matrix Estimation of Heterogeneous Clutter in High-Resolution Polarimetric SAR Images

Vasile

Ovarlez

Pascal

et al. 2010

IEEE Trans. Geosci. Remote Sensing

100

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This paper presents an application of the recent advances in the field of Spherically Invariant Random Vectors (SIRV) modelling for coherency matrix estimation in heterogeneous clutter. The complete description of the POLSAR data set is achieved by estimating the span and the normalized coherency independently. The normalized coherency describes the polarimetric diversity, while the span indicates the total received power. The main advantages of the proposed Fixed Point estimator are that it does not require any "a priori" information about the probability density function of the texture (or span) and it can be directly applied on adaptive neighborhoods. Interesting results are obtained when coupling this Fixed Point estimator with an adaptive spatial support based on the scalar span information. Based on the SIRV model, a new maximum likelihood distance measure is introduced for unsupervised POLSAR classification. The proposed method is tested with both simulated POLSAR data and airborne POLSAR images provided by the RAMSES system. Results of entropy/alpha/anisotropy decomposition, followed by unsupervised classification, allow to discuss the use of the normalized coherency and the span as two separate descriptors of POLSAR data sets.

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“…This aircraft, based at the Nimes-Garons airport (Southeast of France), is operated by AVdef, a French private company. It offers 2 1 2 hours of autonomy and flies at a speed range between 160kt and 300kt, up to flight level 300. To support the SETHI system, the aircraft was equipped with specific wiring in the wings and a more powerful electrical generator.…”

Section: A General Architecturementioning

confidence: 99%

SETHI: The Flying Lab

Bruyant¹,

Dreuillet²,

Plessis³

et al. 2011

International Journal of Electronics and Telecommunications

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SETHI: The Flying Lab This paper presents the new-generation test bench SETHI, developed by ONERA, the French Aerospace Lab. SETHI is a medium range platform dedicated to environmental, scientific and security applications. The first part of this paper describes the system architecture, the development state and the future capabilities. A set of recent significant results are presented: these results cover various applications, such as high spatial resolution imaging, change detection between two acquisitions, biomass measurement in the rain forest, bistatic imaging and innovative measurements, such as air-to-air imaging or circular imaging.

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The ONERA RAMSES SAR: Latest Significant Results and Future Developments

Cited by 21 publications

References 7 publications

Maximum Likelihood Classification of High-Resolution Sar Images in Urban Area

Maximum Likelihood Classification of High-Resolution Sar Images in Urban Area

Coherency Matrix Estimation of Heterogeneous Clutter in High-Resolution Polarimetric SAR Images

SETHI: The Flying Lab

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