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Communication dans un congrès

Saliency Attention and SIFT Keypoints Combination for Automatic Target Recognition on MSTAR dataset

Ayoub Karine 1 Abdelmalek Toumi 2, 3 Ali Khenchaf 4, 3 Mohammed El Hassouni 1
2 Lab-STICC_ENSTAB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 Pôle STIC_REMS
ENSTA Bretagne - École Nationale Supérieure de Techniques Avancées Bretagne
4 Lab-STICC_ENSTAB_MOM_PIM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : This paper aims to present a novel method for automatic target recognition based on synthetic aperture radar (SAR) images. In order to describe a region of interest (target area), we use a saliency attention model. Then, the produced saliency map is used as a mask on SAR image in order to separate the ground target from the background. After that, we calculate the scale invariant feature transform (SIFT) descriptors of the transformed SAR image. In this way, we maintain only the SIFT keypoints located in the salient region. This strategy leads not only to reduce the dimensionality but also enhances its discriminative power. For recognition step, a matching approach between vector descriptors of unknown image target and all known images stored in training data set is adopted. To validate the proposed approach, MSTAR data set is used. The obtained experimental results show that our approach can effectively describe a SAR image, and obviously improve the recognition rate.
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https://hal-ensta-bretagne.archives-ouvertes.fr/hal-01657530
Contributeur : Abdelmalek Toumi <>
Soumis le : mercredi 6 décembre 2017 - 18:40:10
Dernière modification le : mercredi 24 juin 2020 - 16:19:51

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ATSIP2017_AKARINE.pdf
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Ayoub Karine, Abdelmalek Toumi, Ali Khenchaf, Mohammed El Hassouni. Saliency Attention and SIFT Keypoints Combination for Automatic Target Recognition on MSTAR dataset . 3rd International Conference on Advanced Technologies for Signal and Image Processing - ATSIP’2017, Mar 2017, fez Morocco. ⟨10.1109/ATSIP.2017.8075558⟩. ⟨hal-01657530⟩

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