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An efficient classification method of fully polarimetric SAR image based on polarimetric features and spatial features

机译：基于极化特征和空间特征的全极化SAR图像有效分类方法

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Polarimetric SAR(PolSAR) has played more and more important roles in earth observation. Polarimetric SAR image classification is one of the key problems in the PolSAR image interpretation. In this paper, based on the scattering properties of fully polarimetric SAR data, combing the statistical characteristics and neighborhood information, an efficient method of fully polarimetric SAR image classification is proposed. In the method, polarimetric scattering characteristics of fully polarimetric SAR image is used, and in the denoised total power image of polarimetric SAR, Span, the texture features of gray level co-occurrence matrix are extracted at the same time. Finally, the polarimetric information and texture information are combined for fully polarimetric SAR Image classification by clustering algorithm. The experimental results show that better classification results can be obtained in the Radarsat-2 data with the proposed method.
机译：极化SAR（PolSAR）在地球观测中起着越来越重要的作用。极化SAR图像分类是PolSAR图像解释中的关键问题之一。本文基于全极化SAR数据的散射特性，结合统计特征和邻域信息，提出了一种有效的全极化SAR图像分类方法。该方法利用了全极化SAR图像的极化散射特性，在极化SAR的去噪总功率图像Span中，同时提取了灰度共生矩阵的纹理特征。最后，结合聚类算法将极化信息和纹理信息进行全极化SAR图像分类。实验结果表明，提出的方法可以在Radarsat-2数据中获得更好的分类结果。

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