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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Published in | Digital signal processing Vol. 20; no. 5; pp. 1365 - 1378 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Inc
01.09.2010
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Subjects | |
Online Access | Get full text |
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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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1051-2004 1095-4333 |
DOI: | 10.1016/j.dsp.2009.10.014 |