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The feasibility analysis of applying NMF in SAR target recognition

机译:在SAR目标识别中应用NMF的可行性分析

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Synthetic Aperture Radar automatic target recognition (SAR ATR) plays an important role both in national defense and civil applications. In this paper, the recognition process via Non-negative Matrix Factorization (NMF) is visualized and analyzed to verify the feasible of applying NMF in SAR ATR. As a matrix decomposition method, NMF can obtain part-based representation, and can be applied in SAR ATR as a pattern recognition feature. However, most of researches don't consider the intrinsic character and the physical meaning of NMF when using it in SAR images. This paper starts from the basic decomposition formulas, explores the physical meaning of the base matrix and the encoding matrix, and obtains the linear relationship between the sample to be recognized and the train samples. The experiments show that NMF can obtain better performance than other pattern features, and can obtain spare representation with practical physical meaning.
机译:合成孔径雷达自动目标识别(SAR ATR)在国防和民用应用中起着重要作用。在本文中,通过非负矩阵分解(NMF)的识别过程被可视化和分析,以验证在SAR ATR中应用NMF的可行性。作为矩阵分解方法,NMF可以获得基于部分的表示,并且可以在SAR ATR中应用于模式识别特征。然而,大多数研究都不考虑在SAR图像中使用时的内在字符和NMF的物理含义。本文从基本分解公式开始,探索基本矩阵和编码矩阵的物理含义,并获得要识别的样本与列车样本之间的线性关系。实验表明,NMF可以获得比其他模式特征更好的性能,并且可以使用实际的物理意义获得备用表示。

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