资源简介

摘要： 提出了一种组合中间层特征(Middle Level Feature,MLF)和支持向量机(Support Vector Machine,〖JP2〗SVM)的全极化合成孔径雷达(Synthetic Aperture Radar,SAR)监督地物分类方法。选择监督方法的目的是直接区分实际地物类别,中间层特征在非监督聚类结果中获取,用于跨越底层特征与地物类别间的语义鸿沟。统计以某像素为中心的特征支持区域内各“中间成分”的占比作为该像素的MLF。这里“中间成分”对应于基于底层极化特征得到的非监督聚类类别。在覆盖武汉地区的Radarsat\|2全极化数据上,与基于经典全极化特征的SVM监督分类方法进行了对比,研究了不同中间成分获取方法以及特征支持窗口对于分类性能的影响,结果显示:该方法有很好的性能并有进一步提升的空间。〖JP〗 关键词： 全极化SAR;  支持向量机;  中间层特征;  中间成分     Abstract: A supervised method combining Middle Level Feature (MLF) with Support Vector Machine (SVM) was proposed for land\|use classification of full polarization Synthetic Aperture Radar (SAR) image.Supervised method is chosen to directly distinguish the actual land-use categories.The MLF that is used for striding over the semantic gap between the low-level polarization scattering characteristics and the high\|level semantics of land-use categories is got from the result of classic unsupervised classification methods for full polarization SAR image.The MLF of a pixel is calculated by counting the frequency of the middle-components in a feature supporting region centered on the pixel.Here the middle-components refer to the unsupervised clustering categories obtained from the low\|level polarization characteristics.The proposed method is tested on a Radarsat-2 full polarization data covering WUHAN area and good classification performance and potential of further improvement are shown.The comparison between the supervised classification method combining SVM and the classic polarization characteristics is given.The proposed method and different methods for getting the middle-components and feature supporting windows with different size are studied on their impact on the final classification performance.

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