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<p>Dynamic threshold neural P (DTNP) systems are neural-like computing models. This paper discusses how to use DTNP systems to propose a new change detection for SAR images. DTNP systems have two intrinsic and recognizable mechanisms: dynamic threshold and spiking mechanisms. The two machanisms are used to a new region growing algorithm. To obtain the optimal seed points of regional growth, particle swarm optimization (PSO) algorithmis used, and then DTNP systems are used to control the regional growth according to these seed points. Simulation experiments are carried out on four SAR image datasets, and the proposed method is evaluated on two metrics and compared with several state-of-the-art or baseline change detection methods. The comparison results show the effectiveness and advantage of the proposed method for change detection of SAR images.</p>

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Unsupervised change detection using dynamic threshold neural P systems for SAR images

  • 期刊论文

  • 发表日期：

    2025年5月12日

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  • Rikong Lugu,
  • Qian Yang,
  • Hong Peng

摘要

Dynamic threshold neural P (DTNP) systems are neural-like computing models. This paper discusses how to use DTNP systems to propose a new change detection for SAR images. DTNP systems have two intrinsic and recognizable mechanisms: dynamic threshold and spiking mechanisms. The two machanisms are used to a new region growing algorithm. To obtain the optimal seed points of regional growth, particle swarm optimization (PSO) algorithmis used, and then DTNP systems are used to control the regional growth according to these seed points. Simulation experiments are carried out on four SAR image datasets, and the proposed method is evaluated on two metrics and compared with several state-of-the-art or baseline change detection methods. The comparison results show the effectiveness and advantage of the proposed method for change detection of SAR images.