基于极化SAR数据的地物要素提取

Extraction of the Primary Object Based on the PolSAR Data

本文提出了一个基于极化SAR数据进行重要地物要素提取的方法,首先通过经典的特征分解方法得到散射目标的极化特征,然后结合基于Wishart的K-means非监督分类方法和基于最大似然的监督分类方法得到分类结果,经过分类后处理,最后提取各地物要素,生成地表覆盖图。实测极化SAR数据结果验证了方法的有效性。

Abstract In this paper, we have proposed a good methed to extract the primary object based on the PolSAR data. The methed first applies the classic target decomposition theorems to get the polarimetric properties of the scattering targets. Then it combins the wishart unsupervised classification method based on K-means algorithm and the supervised maximum likelihood classification method to get the classifcaiton maps. After post-processing, we will extract the terrain factors and general the land cover map. Through the experimental results, we have proved the effectiveness of our method.