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FUSION OF DEEP LEARNING ARCHITECTURES FOR ENHANCED TARGET RECOGNITION ON SAR IMAGES

Cheikh, K ; Aitahcene, R ; Toumi, A ; Hammoudi, Z

Jordanian journal of computers and information technology (Online), 2023-12, Vol.9 (4), p.347-359 [Periódico revisado por pares]

Amman: Scientific Research Support Fund of Jordan Princess Sumaya University for Technology

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  • Título:
    FUSION OF DEEP LEARNING ARCHITECTURES FOR ENHANCED TARGET RECOGNITION ON SAR IMAGES
  • Autor: Cheikh, K ; Aitahcene, R ; Toumi, A ; Hammoudi, Z
  • Assuntos: automatic target recognition ; decision fusion ; deep learning ; Majority rule ; Neural networks ; synthetic aperture radar images
  • É parte de: Jordanian journal of computers and information technology (Online), 2023-12, Vol.9 (4), p.347-359
  • Descrição: In various applications of radar imagery, one of the fundamental problems is mainly linked to the analysis and interpretation of the images provided, in particular the recognition of moving and/or fixed targets. This task has become more difficult due to the large volume of radar data. This led to the use of automatic-processing and target-recognition methods. The aim of this study is to explore data fusion in SAR (Synthetic Aperture Radar) image classifiers. To this end, we propose a new approach to combine three CNN (Convolutional Neural Network) architectures with several fusion rules. First, we perform a training process of three deep-learning architectures; namely, the basic CNN, the Xception and the AlexNet architectures. Then, two fusion techniques are proposed. The first one deals with the majority rule and the second uses a neural network to combine the decision outputs obtained from three elementary classifiers to achieve the final decision. To evaluate and validate the proposed approach, the MSTAR (Moving and Stationary Target Acquisition and Recognition) dataset is used. The obtained performances of the fusion techniques improve the recognition rate with a final accuracy of 99.59% for the majority rule and 99.51% for the neural network-based rule, which surpasses the accuracy of each individual CNN.
  • Editor: Amman: Scientific Research Support Fund of Jordan Princess Sumaya University for Technology
  • Idioma: Árabe;Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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