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An ISAR Image Segmentaion Method Based on MMARP Model

机译:基于MRP模型的ISAR图像分割方法

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This paper presents a method of unsupervised segmentation for Inverse synthetic aperture radar (ISAR) images. Firstly, a generalized multiresolution likelihood ratio (GMLR) is defined, which classifies different kinds of signals more accurately than classical likelihood ratio by fusing more and different signal features. For our ISAR image segmentation application, multiresolution stochastic structure inherent in ISAR imagery is well captured by a set of multiscale autoregressive (MAR) models. Secondly, good parameter estimates of GMLR can be obtained by estimating several mixture multiscale autoregressive prediction (MMARP) models using EM algorithm. Thirdly, considering the independence assumption of maximum likelihood estimation of parameter by EM algorithm and reduction of the segmentation time, we present the bootstrap sampling techniques applied above algorithm. Finally, Experimental results demonstrate that our algorithm performs fairly well.
机译:本文提出了一种逆向合成孔径雷达(ISAR)图像的无监督分割方法。首先,定义了一种通用的多分辨率似然比(GMLR),它通过融合更多和不同的信号特征,比经典似然比更准确地对不同种类的信号进行分类。对于我们的ISAR图像分割应用,ISAR图像中固有的多分辨率随机结构可以通过一组多尺度自回归(MAR)模型很好地捕获。其次,可以通过使用EM算法估计几个混合多尺度自回归预测(MMARP)模型来获得良好的GMLR参数估计。第三,考虑到EM算法对参数的最大似然估计的独立性假设以及分割时间的减少,我们提出了在上述算法上应用的自举采样技术。最后,实验结果表明我们的算法性能很好。

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