A two-pass unsupervised clustering algorithm for polarimetric SAR image segmentation

Jianchao Fan, Jun Wang

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

4 Citations (Scopus)

Abstract

This paper proposes a two-pass clustering algorithm with a combination of the linear assignment and fuzzy C-means methods (FCM) for polarimetric SAR (PolSAR) image segmentation. To avoid the inconsistency of clustering results from the fuzzy C-means method with random initialization, the linear assignment method with the least similar cluster representatives is applied first to generate initial clusters, and then followed with the FCM method. Appropriate initial clustering centres adjacent to the actual final clustering centres can be found to promote the convergence speed of the overall iterative process and drastically reduce the calculation time. Otherwise, the modified algorithm is updated from multidimensional data analysis to PolSAR image clustering. This approach is applied to four well-known practical UCI datasets and public PolSAR image segmentation. The results are compared with those from the fuzzy C-means method with other initialization methods. It is shown that the two pass approach consistently results in the best clustering results. The application results on PolSAR image segmentation are also demonstrated.

Original languageEnglish
Title of host publicationOCEANS 2013 MTS/IEEE Bergen
Subtitle of host publicationThe Challenges of the Northern Dimension
DOIs
Publication statusPublished - 2013
Externally publishedYes
EventOCEANS 2013 MTS/IEEE Bergen: The Challenges of the Northern Dimension - Bergen, Norway
Duration: 10 Jun 201313 Jun 2013

Publication series

NameOCEANS 2013 MTS/IEEE Bergen: The Challenges of the Northern Dimension

Conference

ConferenceOCEANS 2013 MTS/IEEE Bergen: The Challenges of the Northern Dimension
Country/TerritoryNorway
CityBergen
Period10/06/1313/06/13

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