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Superpixel-based segmentation of polarimetric SAR images through two-stage merging
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
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2019 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 11, no 4, article id 402Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI AG , 2019. Vol. 11, no 4, article id 402
Keywords [en]
Edge penalty, KummerU distribution, Polarimetric SAR (PolSAR), Region merging, Segmentation, Energy dissipation, Image segmentation, Iterative methods, Pixels, Polarimeters, Radar imaging, Superpixels, Synthetic aperture radar, Computation efficiency, Polarimetric SAR, Region-merging, Segmentation accuracy, Segmentation methods, Statistical modeling, Merging
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Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:kth:diva-248206DOI: 10.3390/rs11040402ISI: 000460766100032Scopus ID: 2-s2.0-85062555619OAI: oai:DiVA.org:kth-248206DiVA, id: diva2:1304515
Note

QC 20190412

Available from: 2019-04-12 Created: 2019-04-12 Last updated: 2019-04-12Bibliographically approved

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Wang, WeiBan, Yifang

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