Combined use of MAP estimation and K-means classifier for speckle noise filtering in SAR images

Author

Medeiros, Fátima N S ; Mascarenhas, Nelson D A ; Costa, Luciano Da F

Author_Institution

UFCe-Dee, Fortaleza, Brazil

fYear

1998

fDate

5-7 Apr 1998

Firstpage

250

Lastpage

255

Abstract

The main purpose of this work is to study and implement a maximum a posteriori (MAP) filter combined with the K-means algorithm in order to reduce speckle noise in SAR images. The K-means algorithm over Li´s (1988) coefficient is used to classify the noisy image in regions of homogenous statistics. This kind of information is used as a guide for choosing the best window size for parameter estimation in the MAP filtering. This paper is based on the multiplicative model for speckle and considers different densities to describe the “a priori” knowledge. It suggests a new adaptive filtering algorithm based on the MAP approach and clustering

Keywords

adaptive filters; image classification; interference suppression; maximum likelihood estimation; noise; radar imaging; radar interference; remote sensing by radar; speckle; synthetic aperture radar; K-means classifier; MAP estimation; MAP filtering; SAR images; adaptive filtering algorithm; best window size; clustering; densities; homogenous statistics region; maximum a posteriori filter; multiplicative model; noisy image; parameter estimation; speckle noise filtering; Adaptive filters; Clustering algorithms; Filtering; Noise level; Noise reduction; Nonlinear filters; Parameter estimation; Scattering; Speckle; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Analysis and Interpretation, 1998 IEEE Southwest Symposium on

Conference_Location

Tucson, AZ

Print_ISBN

0-7803-4876-1

Type

conf

DOI

10.1109/IAI.1998.666894

Filename

666894