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Remote Sens. 2017, 9(6), 570; doi:10.3390/rs9060570

Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach

Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
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Academic Editors: Timo Balz, Uwe Soergel, Mattia Crespi, Batuhan Osmanoglu, Zhenhong Li and and Prasad S. Thenkabail
Received: 24 February 2017 / Revised: 23 May 2017 / Accepted: 4 June 2017 / Published: 6 June 2017
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)
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Abstract

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. View Full-Text
Keywords: polarimetric synthetic aperture radar (PolSAR); segmentation; high-resolution; fractal net evolution approach (FNEA); G0 distribution; simple linear iterative clustering (SLIC); multi-feature; superpixels polarimetric synthetic aperture radar (PolSAR); segmentation; high-resolution; fractal net evolution approach (FNEA); G0 distribution; simple linear iterative clustering (SLIC); multi-feature; superpixels
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, Q.; Li, L.; Xu, Q.; Yang, S.; Shi, X.; Liu, X. Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach. Remote Sens. 2017, 9, 570.

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