SAR imagery segmentation by statistical region growing and hierarchical merging

被引：39

作者：

Carvalho, E. A. ^{[1
]}

Ushizima, D. M. ^{[2
,3
]}

Medeiros, F. N. S. ^{[1
]}

Martins, C. I. O. ^{[1
]}

Marques, R. C. P. ^{[1
]}

Oliveira, I. N. S. ^{[4
]}

机构：

[1] Univ Fed Ceara, Grp Proc Imagens, Dept Teleinformat DETI, Fortaleza, Ceara, Brazil

[2] Lawrence Berkeley Natl Lab, Math Grp, Berkeley, CA USA

[3] Lawrence Berkeley Natl Lab, Visualizat Grp, Berkeley, CA USA

[4] Univ Fed Ceara, Dept Elect Engn, Fortaleza, Ceara, Brazil

来源：

DIGITAL SIGNAL PROCESSING | 2010年 / 20卷 / 05期

关键词：

SAR image segmentation; Region growing; Hierarchical merging; Hypothesis testing; Speckle noise; EDGE; NOISE; ENHANCEMENT; DETECTOR; MODEL;

D O I：

10.1016/j.dsp.2009.10.014

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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. (C) 2009 Elsevier Inc. All rights reserved.

引用

页码：1365 / 1378

页数：14

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