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Open AccessArticle

Superpixel-Based Segmentation of Polarimetric SAR Images through Two-Stage Merging

1
Division of Geoinformatics, KTH Royal Institute of Technology, 10044 Stockholm, Sweden
2
National Key Laboratory of Science and Technology on ATR, National University of Defense Technology, Changsha 410073, China
3
National Innovation Institute of Technology, Beijing 100091, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(4), 402; https://doi.org/10.3390/rs11040402
Received: 14 January 2019 / Revised: 12 February 2019 / Accepted: 13 February 2019 / Published: 16 February 2019
(This article belongs to the Special Issue Superpixel based Analysis and Classification of Remote Sensing Images)
Image segmentation plays a fundamental role in image understanding and region-based applications. This paper presents a superpixel-based segmentation method for Polarimetric SAR (PolSAR) data, in which a two-stage merging strategy is proposed. First, based on the initial superpixel partition, the Wishart-merging stage (WMS) simultaneously merges the regions in homogeneous areas. The edge penalty is combined with the Wishart energy loss to ensure that the superpixels to be merged are from the same land cover. The second stage follows the iterative merging procedure, and applies the doubly flexible KummerU distribution to better characterize the resultant regions from WMS, which are usually located in heterogeneous areas. Moreover, the edge penalty and the proposed homogeneity penalty are adopted in the KummerU-merging stage (KUMS) to further improve the segmentation accuracy. The two-stage merging strategy applies the general statistical model for the superpixels without ambiguity, and more advanced model for the regions with ambiguity. Therefore, the implementing efficiency can be improved based on the WMS, and the accuracy can be increased through the KUMS. Experimental results on two real PolSAR datasets show that the proposed method can effectively improve the computation efficiency and segmentation accuracy compared with the classical merging-based methods. View Full-Text
Keywords: polarimetric SAR (PolSAR); segmentation; KummerU distribution; region merging; edge penalty polarimetric SAR (PolSAR); segmentation; KummerU distribution; region merging; edge penalty
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MDPI and ACS Style

Wang, W.; Xiang, D.; Ban, Y.; Zhang, J.; Wan, J. Superpixel-Based Segmentation of Polarimetric SAR Images through Two-Stage Merging. Remote Sens. 2019, 11, 402.

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