Polarimetric SAR image segmentation based on spatially constrained kernel fuzzy C-means clustering

Jianchao Fan, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

A spatially constrained kernel fuzzy C-means (SCKFCM) algorithm is represented for polarimetric SAR (PolSAR) remote sensing image segmentation in this paper. Compared with classic fuzzy C-means (FCM) algorithm, kernel method could perform the nonlinear mapping from the original space to kernel space. Thus, SCKFCM is not impacted by the remote sensing image data distribution. Furthermore, in order to overcome the affection of speckle noises, the spatial constraint item is added in the objective function, which would improve the image segmentation accuracy effectively. The experiment results on PolSAR image segmentation demonstrate the validity of proposed SCKFCM approach.

Original languageEnglish
Title of host publicationMTS/IEEE OCEANS 2015 - Genova
Subtitle of host publicationDiscovering Sustainable Ocean Energy for a New World
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479987368
DOIs
Publication statusPublished - 17 Sep 2015
Externally publishedYes
EventMTS/IEEE OCEANS 2015 - Genova - Genova, Italy
Duration: 18 May 201521 May 2015

Publication series

NameMTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World

Conference

ConferenceMTS/IEEE OCEANS 2015 - Genova
Country/TerritoryItaly
CityGenova
Period18/05/1521/05/15

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