关键词: Freeman分解, 雷达植被指数, 支持向量机, Wishart监督分类, 极化SAR

Abstract: Based on the coherence matrix of the image, the characteristic parameters, namely the radar vegetation index （RVI）, were extracted. Then the Freeman decomposition of the covariance matrix of the image was performed, and three scattering mechanism parameters were obtained, which are body scattering, surface scattering and dihedral scattering. These parameters are then combined into a support vector machine （SVM） to classify the polarimetric SAR images and compare them with the Wishart supervised classification. The results show that the radar vegetation index can improve the classification accuracy of vegetation, and the classification accuracy of this method was significantly higher than the Wishart supervised classification.

Key words: Freeman decomposition, radar vegetation index, support vector machine, Wishart supervised classification, polarized SAR