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Remote Sens. 2016, 8(8), 619; doi:10.3390/rs8080619

Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images

Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
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Academic Editors: Heiko Balzter, Zhong Lu and Prasad S. Thenkabail
Received: 22 April 2016 / Revised: 13 July 2016 / Accepted: 21 July 2016 / Published: 26 July 2016
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Abstract

Classification techniques play an important role in the analysis of polarimetric synthetic aperture radar (PolSAR) images. PolSAR image classification is widely used in the fields of information extraction and scene interpretation or is performed as a preprocessing step for further applications. However, inherent speckle noise of PolSAR images hinders its application on further classification. A novel supervised superpixel-based classification method is proposed in this study to suppress the influence of speckle noise on PolSAR images for the purpose of obtaining accurate and consistent classification results. This method combines statistical information with spatial context information based on the stochastic expectation maximization (SEM) algorithm. First, a modified simple linear iterative clustering (SLIC) algorithm is utilized to generate superpixels as classification elements. Second, class posterior probabilities of superpixels are calculated by a K distribution in iterations of SEM. Then, a neighborhood function is defined to express the spatial relationship among adjacent superpixels quantitatively, and the class posterior probabilities are updated by this predefined neighborhood function in a probabilistic label relaxation (PLR) procedure. The final classification result is obtained by the maximum a posteriori decision rule. A simulated image, a spaceborne RADARSAT-2 image, and an airborne AIRSAR image are used to evaluate the proposed method, and the classification accuracy of our proposed method is 99.28%, 93.16% and 89.70%, respectively. The experimental results indicate that the proposed method obtains more accurate and consistent results than other methods. View Full-Text
Keywords: polarimetric synthetic aperture radar (PolSAR); image classification; superpixels; probabilistic label relaxation (PLR); K distribution polarimetric synthetic aperture radar (PolSAR); image classification; superpixels; probabilistic label relaxation (PLR); K distribution
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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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Xu, Q.; Chen, Q.; Yang, S.; Liu, X. Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images. Remote Sens. 2016, 8, 619.

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