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Superpixel Segmentation of Polarimetric SAR Images Based on Integrated Distance Measure and Entropy Rate Method
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.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
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2017 (English)In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 10, no 9, p. 4045-4058, article id 7942039Article in journal (Refereed) Published
Abstract [en]

This paper proposes to integrate two different distances to measure the dissimilarity between neighboring pixels in PolSAR images, and introduces the entropy rate method into PolSAR image superpixel segmentation. Since the Gaussian model is commonly used for homogeneous scenes and less suitable for heterogeneous scenes, we adopt the spherically invariant random vector (SIRV) model to describe the back-scattering characteristics in heterogeneous areas. Moreover, a directional span-driven adaptive (DSDA) region is proposed such that it contains independent and identically distributed samples only, thus it can obtain accurate estimation of the distribution parameters. Using the DSDA region, the Wishart distance and SIRV distance are calculated, and then combined together through a homogeneity measurement. Therefore, the integrated distance takes advantage of the SIRV model and the Gaussian model, and suits both homogeneous and heterogeneous areas. Finally, based on the integrated distance, the superpixel segments are generated using the entropy rate framework. The experimental results on ESAR and PiSAR L-band datasets show that the proposed method can generate homogeneity-adaptive segments, resulting in smooth representation of the land covers in homogeneous areas, and better preserved details in heterogeneous areas.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers , 2017. Vol. 10, no 9, p. 4045-4058, article id 7942039
Keywords [en]
Entropy rate, polarimetric SAR (PolSAR), segmentation, spherically invariant random vecor (SIRV), superpixel, Backscattering, Entropy, Gaussian distribution, Image segmentation, Pixels, Signal theory, Synthetic aperture radar, Accurate estimation, Distance measure, Distribution parameters, Entropy rates, Gaussian model, Polarimetric SAR, Random vectors, Superpixel segmentations, Radar imaging
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-216179DOI: 10.1109/JSTARS.2017.2708418ISI: 000412626400021Scopus ID: 2-s2.0-85020381501OAI: oai:DiVA.org:kth-216179DiVA, id: diva2:1160097
Note

QC 20171124

Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2017-11-24Bibliographically approved

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Wang, WeiXiang, DeliangBan, Yifang

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