An Integrated of Multi-feature Segmentation Method Polarimetric SAR Images
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摘要: 提出了一种综合利用极化特征、统计特征和几何形状特征的全极化SAR图像分割方法。该方法采用分形网络演化算法思想，基于相干矩阵Pauli分解构建对象间的极化特征相似性准则，根据相干矩阵的Wishart分布假设构建对象间的统计特征相似性准则;制定对象合并过程中多特征的综合策略，通过极化特征增强及权重调整统一各类特征异质度的水平，最终建立全极化SAR图像多特征综合分割流程。实验结果表明，该方法能有效抑制斑点噪声，地物边界分割准确，特别是对具有均质纹理的农田、水体等分割效果较好。Abstract: In this paper，we proposed a new segmentation method which integrates polarimetric，statistical distribution and geometric shape features of polarimetric SAR image based on the Fractal Network Evolution Algorithm(FNEA).Firstly，the similarity criterion of polarimetric features between adjacent objects was acquired based on the Pauli decomposition，while the similarity criterion of statictical feature was constructed via the coherency matrix Wishart distribution hypothesis. Secondly，a multi-feature integration strategy in objects merging was established，and polarimetric feature values were stretched in advance to make the heterogeneities of polarimetric，statistic distribution and shape features between adjacent objects to be at the similar level. Then，an integrated multi-feature segmentation flow was built according to the above processes. Lastly，this method was verified with RADA-RSAT-2 image of Altona and L band ESAR image of Oberpfaffenhofen，suggesting that it can effetetively reduce the speckle effects and obtain accurate segmentation results，especially in the homogeneous texture areas like farmland and lakes.
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