基于ART2神经网络的SAR图像识别研究

Xiaoming Ye, Wei Gao, Yi Wang, Xiaoguang Hu
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引用次数: 7

摘要

ART2是一种基于自适应共振理论的自组织神经网络。它利用竞争学习和自稳定机制进行识别,能够在有噪声和无监督的动态环境中进行自我学习。它的学习过程可以快速识别已学习的模型,并快速适应新的未知对象。提出了一种基于PCA和ART2神经网络的合成孔径雷达自动目标识别方法。该方法以主成分为样本特征,利用ART2神经网络对SAR图像进行识别。在MSTAR SAR数据集上的实验结果表明,该算法具有较好的识别和泛化性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on SAR images recognition based on ART2 neural network
ART2 is a kind of self-organizing neural network which is based on adaptive resonance theory. It carries out the recognition by using competive learning and self-steady mechanism, and can learn by itself in dynamic environment with noise and without supervision. Its learning process can recognize learned models fastly and be adapted to new unknown objects rapidly. SAR ATR (Synthetic Aperture Radar Automatic Target Recognition) approach based on PCA and ART2 neural network is proposed in this paper. It takes the principal components as sample features, and then ART2 neural network is used to recognize SAR images. Experimental results with MSTAR SAR data sets show a better performance of recognition and generalization.
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