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Title: SAR Imagery Segmentation by Statistical Region Growing and Hierarchical Merging

Abstract

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.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
Computational Research Division
OSTI Identifier:
986490
Report Number(s):
LBNL-3844E
TRN: US201017%%360
DOE Contract Number:  
DE-AC02-05CH11231
Resource Type:
Journal Article
Journal Name:
Digital Signal Processing
Additional Journal Information:
Journal Name: Digital Signal Processing
Country of Publication:
United States
Language:
English
Subject:
42; ALGORITHMS; OPTIMIZATION; RADAR; TARGETS; SAR

Citation Formats

Ushizima, Daniela Mayumi, Carvalho, E A, Medeiros, F N.S., Martins, C I.O., Marques, R C.P., and Oliveira, I N.S. SAR Imagery Segmentation by Statistical Region Growing and Hierarchical Merging. United States: N. p., 2010. Web.
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. United States.
Ushizima, Daniela Mayumi, Carvalho, E A, Medeiros, F N.S., Martins, C I.O., Marques, R C.P., and Oliveira, I N.S. Sat . "SAR Imagery Segmentation by Statistical Region Growing and Hierarchical Merging". United States. https://www.osti.gov/servlets/purl/986490.
@article{osti_986490,
title = {SAR Imagery Segmentation by Statistical Region Growing and Hierarchical Merging},
author = {Ushizima, Daniela Mayumi and Carvalho, E A and Medeiros, F N.S. and Martins, C I.O. and Marques, R C.P. and Oliveira, I N.S.},
abstractNote = {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.},
doi = {},
url = {https://www.osti.gov/biblio/986490}, journal = {Digital Signal Processing},
number = ,
volume = ,
place = {United States},
year = {2010},
month = {5}
}