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Communication dans un congrès

Deep Learning for Target recognition from SAR images

Ali El Housseini Abdelmalek Toumi 1, 2 Ali Khenchaf 3, 2
1 Lab-STICC_ENSTAB_CID_TOMS
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
3 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 deals with the problematic of automatic target recognition (ATR) using Synthetic Aperture Radar (SAR) images. In this work, the Deep Learning (DL) architecture is proposed and applied in order to recognize military vehicles from SAR images. We propose mainly in this work the deep learning algorithms based on convolutional neural network architecture. In the second step and in order to optimize the convolution of DL steps, we propose to use a convo- lutional auto-encoder which may be better suited to image processing. Its use provides several areas of the best results in the presence of noise on shifted and truncated images. To validate our approach, some experimentation results are given and compared. The obtained results show that the proposed approach of DL achieves a height recognition accuracy of 93%.
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https://hal.archives-ouvertes.fr/hal-01656457
Contributeur : Annick Billon-Coat <>
Soumis le : mardi 5 décembre 2017 - 16:33:17
Dernière modification le : mercredi 24 juin 2020 - 16:19:51

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Ali El Housseini, Abdelmalek Toumi, Ali Khenchaf. Deep Learning for Target recognition from SAR images. DAT 2017, Feb 2017, Alger, Algeria. ⟨10.1109/DAT.2017.7889171⟩. ⟨hal-01656457⟩

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