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Aircraft recognition using a statistical model and sparse representation

Ayoub Karine 1, 2, 3 Abdelmalek Toumi 4, 2 Ali Khenchaf 1, 2 Mohammed El Hassouni 5
1 Lab-STICC_ENSTAB_MOM_PIM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
2 Pôle STIC_REMS
ENSTA Bretagne - École Nationale Supérieure de Techniques Avancées Bretagne
4 Lab-STICC_ENSTAB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : This paper presents a novel approach for automatic target recognition (ATR) using inverse synthetic aperture radar (ISAR) images. This proposed approach is mainly com- posed of two steps. In the rst step, we adopt a statisti- cal method to compute a novel target template from fea- ture descriptors. The proposed template is achieved by combining the Gamma statistical parameters of the both dual-tree complex wavelet transform (DT-CWT) coecients and the scale-invariant feature transform (SIFT) descrip- tor. In order to validate the proposed target template, we achieve in the second step the recognition task using a sparse representation-based classi cation (SRC) method. The performance of the proposed approach has been success- fully veri ed using ISAR images reconstructed from anechoic chamber. The experimental results show that the proposed method can achieve a high average accuracy and is signi - cantly superior to the well-known SVM classi er.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01406133
Contributor : Annick Billon-Coat <>
Submitted on : Wednesday, November 30, 2016 - 6:26:45 PM
Last modification on : Wednesday, April 21, 2021 - 11:40:05 AM

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Ayoub Karine, Abdelmalek Toumi, Ali Khenchaf, Mohammed El Hassouni. Aircraft recognition using a statistical model and sparse representation. BDAW ’16, Nov 2016, Blagoevgrad, Bulgaria. ⟨10.1145/3010089.3010134⟩. ⟨hal-01406133⟩

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