SAR Imagery Segmentation by Statistical Region Growing and Hierarchical Merging

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This paper presents an approach to accomplish synthetic aperture radar (SAR) image segmentation, which are corrupted by speckle noise. Some ordinary segmentation techniques may require speckle filtering previously. Our approach performs radar image segmentation using the original noisy pixels as input data, eliminating preprocessing steps, an advantage over most of the current methods. The algorithm comprises a statistical region growing procedure combined with hierarchical region merging to extract regions of interest from SAR images. The region growing step over-segments the input image to enable region aggregation by employing a combination of the Kolmogorov-Smirnov (KS) test with a hierarchical stepwise optimization ... continued below

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Ushizima, Daniela Mayumi; Carvalho, E.A.; Medeiros, F.N.S.; Martins, C.I.O.; Marques, R.C.P. & Oliveira, I.N.S. May 22, 2010.

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This paper presents an approach to accomplish synthetic aperture radar (SAR) image segmentation, which are corrupted by speckle noise. Some ordinary segmentation techniques may require speckle filtering previously. Our approach performs radar image segmentation using the original noisy pixels as input data, eliminating preprocessing steps, an advantage over most of the current methods. The algorithm comprises a statistical region growing procedure combined with hierarchical region merging to extract regions of interest from SAR images. The region growing step over-segments the input image to enable region aggregation by employing a combination of the Kolmogorov-Smirnov (KS) test with a hierarchical stepwise optimization (HSWO) algorithm for the process coordination. We have tested and assessed the proposed technique on artificially speckled image and real SAR data containing different types of targets.

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  • Journal Name: Digital Signal Processing

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  • Report No.: LBNL-3844E
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 986490
  • Archival Resource Key: ark:/67531/metadc1012792

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  • May 22, 2010

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  • Oct. 14, 2017, 8:36 a.m.

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  • Oct. 18, 2017, 10:10 a.m.

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Ushizima, Daniela Mayumi; Carvalho, E.A.; Medeiros, F.N.S.; Martins, C.I.O.; Marques, R.C.P. & Oliveira, I.N.S. SAR Imagery Segmentation by Statistical Region Growing and Hierarchical Merging, article, May 22, 2010; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc1012792/: accessed November 16, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.