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中文摘要：
提出一种无监督分类的机场跑道检测方法。首先利用h/q分解对原图像中所有像素点进行粗分类,建立初始样本模板;利用初始样本模板对原图像进行贝叶斯迭代分类,得到分类图;结合跑道的极化散射特性、弱回波特性及形态学处理方法,从分类图中提取出疑似跑道区域;最终应用跑道的结构特征进一步辨识疑似跑道区域,检测出真实机场跑道目标。通过美国UAVSAR系统采集的多组全极化合成孔径雷达（synthetic aperture radar,SAR）实测数据验证本文算法的有效性,实验结果说明,所提算法能有效、正确地检测出复杂场景下极化SAR图像中的机场跑道区域,且结构完整清晰,虚警率低。
英文摘要：
A new algorithm of runways detection based on unsupervised classification is proposed. F irstly,initial sample templates are constructed from the original image with h/q decomposition. Then, the pixels in theoriginal image are classified again with Bayesian classifier based on the initial sample templates. Thirdly, combiningthe property of polarization scattering and the weak backscattering feature of runways with Morphology filtering,suspected runway areas will be extracted from the above classification image. Using the runways structuralfeatures to identify suspected runway areas ？ the real runway area is detected finally. Multi-look fully polarimetricsynthetic aperture radar （SAR） data acquired by U. S. UAVSAR systems is used to test the proposed algorithm.Experimental results show that the novel algorithm can detect runways effectively from complex scenes of the polarimetricSAR image and has a low false alarm rate and the detected results keep an intact structure and clear outlines.
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