An Integrated of Multi-feature Segmentation Method Polarimetric SAR Images

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.