POLSAR Dominant Scattering Mechanism Clustering and Unsupervised Classification Based on Similarity
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摘要: 极化相似性满足旋转不变性、尺度无关性及有界性，基于此，该文提出一种极化相似性与Freeman模型相结合的POLSAR占优散射归类及非监督聚类方法。实验表明，该方法解决了Freeman分解在分类应用中所存在的问题，相比于直接应用Freeman分解的分类方法，在地物散射特征的描述上更加准确。Abstract: A new POLSAR dominant scattering mechanism clustering and unsupervised classification method is proposed based on polarimetric scattering similarity and Freeman decomposition models. As polarimetric scattering similarity is roll-invariant and nonnegative, the method fixes the problems of Freeman decomposition in classification. Experiment shows that, comparing to the classification method based on Freeman decomposition, the proposed method depicts terrain scattering properties more accurately.
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Key words:
- POLSAR /
- Freeman decomposition /
- Polarization similarity /
- Unsupervised classification
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