Aircraft recognition using a statistical model and sparse representation - Archive ouverte HAL
Communication Dans Un Congrès Année : 2016

Aircraft recognition using a statistical model and sparse representation

Résumé

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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Dates et versions

hal-01406133 , version 1 (30-11-2016)

Identifiants

Citer

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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