SAR imagery segmentation by statistical region growing and hierarchical merging

This paper presents an algorithm to segment synthetic aperture radar (SAR) images, corrupted by speckle noise. Most standard segmentation techniques may require speckle filtering previously. Our approach performs radar image segmentation using the original noisy pixels as input data, i.e. without an...

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Bibliographic Details
Published inDigital signal processing Vol. 20; no. 5; pp. 1365 - 1378
Main Authors Carvalho, E.A., Ushizima, D.M., Medeiros, F.N.S., Martins, C.I.O., Marques, R.C.P., Oliveira, I.N.S.
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.09.2010
Subjects
Algorithms
Digital signal processing
Filtering
Hierarchical merging
Hypothesis testing
Merging
Radar
Region growing
SAR image segmentation
Segmentation
Speckle noise
Synthetic aperture radar
Hypothesis testing
Speckle noise
SAR image segmentation
Region growing
Hierarchical merging
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Summary:This paper presents an algorithm to segment synthetic aperture radar (SAR) images, corrupted by speckle noise. Most standard segmentation techniques may require speckle filtering previously. Our approach performs radar image segmentation using the original noisy pixels as input data, i.e. without any preprocessing step. The algorithm includes a statistical region growing procedure combined with hierarchical region merging. The region growing step oversegments the input radar image, thus enabling region aggregation by employing a combination of the Kolmogorov–Smirnov (KS) test with a hierarchical stepwise optimization (HSWO) algorithm for performance improvement. We have tested and assessed the proposed technique on artificially speckled image and real SAR data.
Bibliography:ObjectType-Article-1
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content type line 23
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2009.10.014