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Communication Dans Un Congrès Année : 2017

Multivariate Linear Time-Frequency modeling and adaptive robust target detection in highly textured monovariate SAR image

Résumé

Usually, in radar imaging, the scatterers are supposed to respond the same way regardless of the angle from which they are viewed and have the same properties within the emitted spectral bandwidth. Nevertheless, new capacities in SAR imaging (large bandwidth, large angular extent) make this assumption obsolete. An original application of the Linear Time-Frequency Distributions (LTFD) in SAR imaging allows to highlight the spectral and angular diver-sities of these reflectors. This methodology allows to transform a monovariate SAR image onto multivariate SAR image. Robust detection schemes in Gaussian or non Gaussian background (Adap-tive Matched Filter (AMF), Adaptive Normalized Matched Filter (ANMF), Anomaly Kelly Detector) associated with classical or robust Covariance Matrix Estimates (Sample Covariance Matrix (SCM), M-estimators) can then be applied exploiting these diver-sities. The combined two-methodologies show their very good performance for target detection.
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Dates et versions

hal-01617057 , version 1 (16-10-2017)

Identifiants

Citer

Jean-Philippe Ovarlez, Guillaume Ginolhac, Abdourrahmane Atto. Multivariate Linear Time-Frequency modeling and adaptive robust target detection in highly textured monovariate SAR image. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017) , Mar 2017, New Orleans, United States. pp.4029 - 4033, ⟨10.1109/ICASSP.2017.7952913⟩. ⟨hal-01617057⟩
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