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Target Recognition in Radar Images Using Weighted Statistical Dictionary-Based Sparse Representation

Ayoub Karine 1, 2, 3 Abdelmalek Toumi 2, 4 Ali Khenchaf 3, 2 Mohammed El Hassouni 5
2 Pôle STIC_REMS
ENSTA Bretagne - École Nationale Supérieure de Techniques Avancées Bretagne
3 Lab-STICC_ENSTAB_MOM_PIM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
4 Lab-STICC_ENSTAB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
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.
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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

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Ayoub Karine, Abdelmalek Toumi, Ali Khenchaf, Mohammed El Hassouni. Target Recognition in Radar Images Using Weighted Statistical Dictionary-Based Sparse Representation. IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2017, 14 (12), pp.2403-2407. ⟨10.1109/LGRS.2017.2766225⟩. ⟨hal-01653569⟩

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