信息论与图像理解：偏振SAR图像的应用
Information Theory and Image Understanding: An Application to Polarimetric SAR Imagery
摘要：
这项工作提出了一个全面的检查使用信息理论的理解极化合成孔径雷达（PolSAR）图像的对比度措施，可以用作检验统计量。由于称为“斑点”的现象，对诸如PolSAR图像等相干照明获得的所有图像都是共同的，因此在处理和分析这些图像时需要精确的建模。缩放的多视复杂Wishart分布已被证明是一个成功的方法，从森林和牧场地区的雷达后向散射建模。依赖于这个模型的分类，分割和图像分析技术已经被设计出来，他们中的许多人采用某种相异性的措施。具体来说，我们引入统计测试，分析在这样的图像的对比度。这些检验基于卡方、Kullback-Leibler、Rényi、Bhattacharyya和Hellinger距离。通过蒙特卡罗实验得到的结果显示，Kullback-Leibler距离作为最好的一个相对于经验的测试大小在几种情况下，其中包括纯和污染的数据。所提出的方法被应用到实际数据，获得的E-SAR传感器周围的我们$β$ ssling，巴伐利亚州，德国。
英文摘要：
This work presents a comprehensive examination of the use of information theory for understanding Polarimetric Synthetic Aperture Radar (PolSAR) images by means of contrast measures that can be used as test statistics. Due to the phenomenon called `speckle', common to all images obtained with coherent illumination such as PolSAR imagery, accurate modelling is required in their processing and analysis. The scaled multilook complex Wishart distribution has proven to be a successful approach for modelling radar backscatter from forest and pasture areas. Classification, segmentation, and image analysis techniques which depend on this model have been devised, and many of them employ some kind of dissimilarity measure. Specifically, we introduce statistical tests for analyzing contrast in such images. These tests are based on the chi-square, Kullback-Leibler, Rényi, Bhattacharyya, and Hellinger distances. Results obtained by Monte Carlo experiments reveal the Kullback-Leibler distance as the best one with respect to the empirical test sizes under several situations which include pure and contaminated data. The proposed methodology was applied to actual data, obtained by an E-SAR sensor over surroundings of We$β$ssling, Bavaria, Germany.
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