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Neural Computing and Applications

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01.12.2013 | Original Article

SAR image despeckling using possibilistic fuzzy C-means clustering and edge detection in bandelet domain

verfasst von: I. Shanthi, M. L. Valarmathi

Erschienen in: Neural Computing and Applications | Sonderheft 1/2013

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Abstract

This paper aims at edge preservation and despeckling of synthetic aperture radar (SAR) images using a novel algorithm comprising edge detection and possibilistic fuzzy C-means clustering (PFCM) in the translation-invariant second-generation bandelet transform (TIBT) domain. The edges from the SAR image are first removed using a canny operator, and TIBT and PFCM clustering are employed to decompose and despeckle the edge-removed image, respectively. The edges are then added to the reconstructed image to obtain an enhanced version of the despeckled image. The quality of the image outperforms other despeckling methods such as K-means and fuzzy C-means that do not use edge preservation techniques. Thus, the proposed algorithm effectively realizes both despeckling and edge preservation techniques.

Vorheriger Artikel Model predictive stabilizer for T–S fuzzy recurrent multilayer neural network models with general terminal weighting matrix

Nächster Artikel A knowledge-based Mamdani fuzzy logic prediction of the motion resistance coefficient in a soil bin facility for clay loam soil

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Titel: SAR image despeckling using possibilistic fuzzy C-means clustering and edge detection in bandelet domain
verfasst von: I. Shanthi
M. L. Valarmathi
Publikationsdatum: 01.12.2013
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1394-y

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