Unsupervised classification of PolInSAR image based on Shannon Entropy Characterization

Wei Yan, Wen Yang, Y. Liu, Hong Sun
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引用次数: 2

Abstract

In this paper, we propose a new method for unsupervised classification of polarimetric synthetic aperture radar interferometry (PolInSAR) images based on Shannon Entropy Characterization. Firstly, we use polarimetric H (entropy) and a parameters to classify the image initially. Then, we reclassify the image according to the span of Shannon Entropy Characterization. Finally, we fuse the results of the two previous steps and merge them to the specified number of clusters. The effectiveness of this method is demonstrated on CETC38 PolInSAR data and E-SAR PolInSAR data.
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基于Shannon熵表征的PolInSAR图像无监督分类
本文提出了一种基于Shannon熵表征的偏振合成孔径雷达干涉图像无监督分类方法。首先,利用偏振熵和a参数对图像进行初始分类。然后,我们根据Shannon熵表征的跨度对图像进行重新分类。最后,我们将前两步的结果融合到指定数量的聚类中。在cet38 PolInSAR数据和E-SAR PolInSAR数据上验证了该方法的有效性。
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