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
2 Pôle STIC_REMS
3 LRIT - Laboratoire de Recherche Informatique et Télécommunications
4 Lab-STICC_ENSTAB_CID_TOMS
5 GSCM-LRIT - Laboratoire de Recherche en Informatique et Télécommunications [Rabat]

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.

Domaines: Traitement du signal et de l'image [eess.SP]

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⟩