利用改进三分量分解与Wishart分类的极化SAR图像建筑提取

本文针对基于Freeman分解的建筑提取方法存在的问题, 提出采用圆极化相关系数实现选择性去取向, 同时引入广义体散射模型, 构建面向建筑提取的改进三分量分解模型, 以准确分析地物的散射特性。在此基础上, 发展了一种综合利用改进三分量分解与Wishart迭代分类算法的极化SAR图像建筑提取方法。使用E-SAR全极化数据的试验结果表明, 本文方法能够有效减少建筑与植被的误分, 并提高建筑信息提取的准确性。

Buildings Extraction from Polarimetric SAR Image Using Improved Three-component Decomposition and Wishart Classification

To address the misclassification issue on buildings extraction based on Freeman decomposition method ,a novel improved three‐component decomposition model is proposed in this paper .By combining the selective de‐orientation derived from the circular polarization correlation coefficient method with the generalized volume scattering model , it can accurately characterize the scattering characteristics of surface features .On this basis ,the complex Wishart iterative classification is introduced to develop a new method of buildings extraction .An E‐SAR L band polarimetric SAR image was used to verify the effective‐ness of this modified algorithm .The experiment result shows it could perform better in distinguishing between oblique buildings and forest ,and consequently improve the accuracy of buildings extraction .