Segmentation techniques play an important role in understanding high-resolution polarimetric synthetic aperture radar (PolSAR) images. PolSAR image segmentation is widely used as a preprocessing step for subsequent classification, scene interpretation and extraction of surface parameters. However, speckle noise and rich spatial features of heterogeneous regions lead to blurred boundaries of high-resolution PolSAR image segmentation. A novel segmentation algorithm is proposed in this study in order to address the problem and to obtain accurate and precise segmentation results. This method integrates statistical features into a fractal net evolution algorithm (FNEA) framework, and incorporates polarimetric features into a simple linear iterative clustering (SLIC) superpixel generation algorithm. First, spectral heterogeneity in the traditional FNEA is substituted by the G0 distribution statistical heterogeneity in order to combine the shape and statistical features of PolSAR data. The statistical heterogeneity between two adjacent image objects is measured using a log likelihood function. Second, a modified SLIC algorithm is utilized to generate compact superpixels as the initial samples for the G0 statistical model, which substitutes the polarimetric distance of the Pauli RGB composition for the CIELAB color distance. The segmentation results were obtained by weighting the G0 statistical feature and the shape features, based on the FNEA framework. The validity and applicability of the proposed method was verified with extensive experiments on simulated data and three real-world high-resolution PolSAR images from airborne multi-look ESAR, spaceborne single-look RADARSAT-2, and multi-look TerraSAR-X data sets. The experimental results indicate that the proposed method obtains more accurate and precise segmentation results than the other methods for high-resolution PolSAR images.

+More

g0 distribution statistical heterogeneity superpixels method polarimetric distance of the pauli rgb composition three realworld highresolution polsar images from airborne multilook esar spaceborne singlelook radarsat2 and multilook terrasarx data subsequent classification scene interpretation and extraction of surface parameters however speckle noise and rich spatial features of heterogeneous regions simulated data segmentation algorithm image objects techniques understanding highresolution polarimetric synthetic aperture radar polsar images polsar image segmentation preprocessing polarimetric features fractal net evolution algorithm fnea framework log likelihood function linear iterative clustering slic superpixel generation cielab color

Qihao Chen,Linlin Li,Qiao Xu,Shuai Yang,Xuguo Shi,Xiuguo Liu,.Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach. 9 (6),.

Qihao Chen,Linlin Li,Qiao Xu,Shuai Yang,Xuguo Shi,Xiuguo Liu,"Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach" 9

Qihao Chen,Linlin Li,Qiao Xu,Shuai Yang,Xuguo Shi,Xiuguo Liu,"Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach"