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Article dans une revue
Target Recognition in Radar Images Using Weighted Statistical Dictionary-Based Sparse Representation
Abstract : In this letter, we present a novel generic approach for radar automatic target recognition in either inverse synthetic aperture radar (ISAR) or synthetic aperture radar (SAR) images. For this purpose, the radar image is described by a statistical modeling in the complex wavelet domain. Thus, the radar image is transformed into a complex wavelet domain using the dual-tree complex wavelet transform. Afterward, the magnitudes of the complex sub-bands are modeled by Weibull or Gamma distributions. The estimated parameters of these models are stacked together to create a statistical dictionary in training step. For the recognition task, we use the weighted sparse representation-based classification method that captures the linearity and locality information of image features. In this context, we propose to use the Kullback-Leibler divergence between the parametric statistical models of training and test sets in order to assign a weight for each training sample. Experiments conducted on both ISAR and SAR images' databases demonstrate that the proposed approach leads to an improvement in the recognition rate.
https://hal.archives-ouvertes.fr/hal-01653569
Contributeur : Annick Billon-Coat <>
Soumis le : vendredi 1 décembre 2017 - 15:35:06
Dernière modification le : mercredi 24 juin 2020 - 16:19:51
Contributeur : Annick Billon-Coat <>
Soumis le : vendredi 1 décembre 2017 - 15:35:06
Dernière modification le : mercredi 24 juin 2020 - 16:19:51