利用边界链编码和HMM进行SAR图像阴影建模和分类
A SAR Image Shadow Modeling and Classification Method Based on HMM and Chain Code
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摘要: 针对利用合成孔径雷达图像中的阴影信息进行目标识别的问题,提出了一种边界链编码和隐马尔可夫模型(HMM)相结合的合成孔径雷达图像目标识别方法。该方法利用链编码技术来描述SAR图像阴影边界的形状,可以很好地反映形状的特性,且计算上很有效;利用HMM统计建模方法对阴影边界的链编码进行建模和分类,从而实现SAR图像的自动目标识别。使用MSTAR数据库中的SAR图像数据对该方法进行了验证和分析,分类结果证明只利用阴影信息进行分类的可行性,且该方法可以有效地实现SAR图像的目标识别。Abstract: The utility of target shadows for automatic target recognition(ATR) in synthetic aperture radar(SAR) imagery is investigated.A method for synthetic aperture radar images target recognition using hidden Markov models and chain code is presented.Shadow shape of SAR image is described by chain code which flects shape characteristic well and computes effectively.The chain codes of target shadow are utilized for hidden Markov modeling.Targets are classified using HMM statistical model.Image samples of targets in MSTAR database are used to verify the method.The results show that the proposed method can enhance the target recognition rate evidently and is an effective method for synthetic aperture radar images target recognition.
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