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VISUAL SALIENT SIFT KEYPOINTS DESCRIPTORS FOR AUTOMATIC TARGET RECOGNITION

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 addresses the problem of automatic target recognition (ATR) using inverse synthetic aperture radar (ISAR) images. In this context, we propose a novel approach for feature extraction to describe precisely an aircraft target from ISAR images. In our approach, a visual attention model is adopted to separate the salient regions from the background. After that, the scale invariant feature transform (SIFT) method is used to extract the keypoints and their descriptors. Then, a local salient feature is built by considering only the keypoints located in the salient region. For the classification step, the support vector machines (SVM) classifier is adopted. To validate the proposed approach, ISAR images database which was collected from anechoic chamber is used.
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https://hal.archives-ouvertes.fr/hal-01406143
Contributor : Annick Billon-Coat <>
Submitted on : Wednesday, November 30, 2016 - 6:38:09 PM
Last modification on : Monday, April 19, 2021 - 4:56:02 PM

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  • HAL Id : hal-01406143, version 1

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Ayoub Karine, Abdelmalek Toumi, Ali Khenchaf, Mohammed El Hassouni. VISUAL SALIENT SIFT KEYPOINTS DESCRIPTORS FOR AUTOMATIC TARGET RECOGNITION. EUVIP, Oct 2016, Marseille, France. ⟨hal-01406143⟩

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