Automated Detection of Hyperbola-Shaped Signature in Subbottom Profiler Sonar Image With Morphological Processing

Subbottom profilers (SBPs) using shipboard sonar can acquire massive amounts of data during exploration missions. Some geological intrusions and artificially buried objects show hyperbola-shaped signatures in the SBP images. These signatures are valuable information, but their detection is time-cons...

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Vydané v:IEEE transactions on geoscience and remote sensing Ročník 62; s. 1 - 14
Hlavní autori: Chen, Pengcheng, Lu, Shaoping, Cai, Chen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Automation
Clustering
Curve fitting
Data acquisition
Fitting
Geoscience and remote sensing
Hyperbola-shaped signature
Hyperbolas
Image acquisition
Image processing
Image resolution
Image segmentation
morphological processing
Morphology
Noise
Profilers
Shape
Signatures
Sonar
Sonar detection
subbottom profiler (SBP)
Template matching
ISSN:0196-2892, 1558-0644
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Abstract Subbottom profilers (SBPs) using shipboard sonar can acquire massive amounts of data during exploration missions. Some geological intrusions and artificially buried objects show hyperbola-shaped signatures in the SBP images. These signatures are valuable information, but their detection is time-consuming. In addition, noise and geometric spread further increase the difficulty of detection. This article proposes an automated detection method of hyperbola-shaped signatures in SBP images by utilizing morphological processing. The proposed method can be summarized into four steps: preprocessing, segmentation, morphological processing, and fitting. The morphological processing is the critical technology in the proposed method, including opening, dilation, and skeletonization. Trend curves of signatures can be outlined without a priori knowledge by exploiting morphological processing. The fitting algorithm can refine the curves further into an analytical curve. We validate the feasibility and effectiveness of the proposed method in field data acquired from the Marianas region. Meanwhile, we demonstrate that the proposed method better detects ill-shaped and large curvature hyperbola-shaped signatures. Compared with the template matching and the column-connection clustering (C3) methods, the proposed method can provide better precision and recall using an optimized threshold. In addition, the proposed method is a general detection methodology that can be applied to any SBP images with proper parameters. In conclusion, the morphological processing presented in this article can be employed as a generic hyperbola-shaped signature detection module in SBP image processing.
AbstractList Subbottom profilers (SBPs) using shipboard sonar can acquire massive amounts of data during exploration missions. Some geological intrusions and artificially buried objects show hyperbola-shaped signatures in the SBP images. These signatures are valuable information, but their detection is time-consuming. In addition, noise and geometric spread further increase the difficulty of detection. This article proposes an automated detection method of hyperbola-shaped signatures in SBP images by utilizing morphological processing. The proposed method can be summarized into four steps: preprocessing, segmentation, morphological processing, and fitting. The morphological processing is the critical technology in the proposed method, including opening, dilation, and skeletonization. Trend curves of signatures can be outlined without a priori knowledge by exploiting morphological processing. The fitting algorithm can refine the curves further into an analytical curve. We validate the feasibility and effectiveness of the proposed method in field data acquired from the Marianas region. Meanwhile, we demonstrate that the proposed method better detects ill-shaped and large curvature hyperbola-shaped signatures. Compared with the template matching and the column-connection clustering (C3) methods, the proposed method can provide better precision and recall using an optimized threshold. In addition, the proposed method is a general detection methodology that can be applied to any SBP images with proper parameters. In conclusion, the morphological processing presented in this article can be employed as a generic hyperbola-shaped signature detection module in SBP image processing.
Author Cai, Chen
Chen, Pengcheng
Lu, Shaoping
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Cites_doi 10.1109/JOE.2021.3107609
10.1109/TGRS.2018.2799586
10.1016/j.autcon.2023.104776
10.3390/electronics9030541
10.1190/1.2732552
10.1029/2022JF006631
10.1130/G51198.1
10.1109/TEVC.2018.2869001
10.1016/S0031-3203(00)00136-9
10.1109/TSMC.1979.4310076
10.3390/rs13214401
10.1016/j.apacoust.2007.05.001
10.1007/s11001-014-9217-9
10.1007/978-3-662-03939-7
10.1017/CBO9780511841552
10.1016/bs.agph.2019.05.001
10.1111/j.1365-2478.1992.tb00552.x
10.1109/JSTSP.2012.2207371
10.1109/ACCESS.2019.2938264
10.1109/TGRS.2015.2462727
10.1038/s41467-020-18361-4
10.1029/138gm10
10.1130/B30149.1
10.1016/j.cageo.2013.04.012
10.1080/2151237X.2007.10129236
10.1007/978-3-642-13835-5
10.1109/TGRS.2009.2012701
10.1109/LGRS.2013.2248119
10.1111/j.1365-2478.1985.tb00778.x
10.1016/j.sigpro.2003.12.010
10.1029/2019EA000680
10.1109/TGRS.2016.2592679
10.1029/2008GC002312
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References ref13
ref35
ref12
ref34
ref15
ref14
ref31
ref30
ref11
ref33
ref10
ref32
ref2
ref1
ref17
ref16
ref19
ref18
ref24
ref23
ref26
ref25
ref20
ref22
ref21
ref27
ref8
ref7
Gonzales (ref28) 2017
ref9
Madsen (ref29) 2004
ref4
ref3
ref6
ref5
References_xml – ident: ref8
  doi: 10.1109/JOE.2021.3107609
– ident: ref11
  doi: 10.1109/TGRS.2018.2799586
– ident: ref16
  doi: 10.1016/j.autcon.2023.104776
– ident: ref14
  doi: 10.3390/electronics9030541
– ident: ref30
  doi: 10.1190/1.2732552
– ident: ref3
  doi: 10.1029/2022JF006631
– ident: ref2
  doi: 10.1130/G51198.1
– ident: ref34
  doi: 10.1109/TEVC.2018.2869001
– ident: ref25
  doi: 10.1016/S0031-3203(00)00136-9
– ident: ref23
  doi: 10.1109/TSMC.1979.4310076
– ident: ref9
  doi: 10.3390/rs13214401
– ident: ref7
  doi: 10.1016/j.apacoust.2007.05.001
– ident: ref26
  doi: 10.1007/s11001-014-9217-9
– ident: ref27
  doi: 10.1007/978-3-662-03939-7
– ident: ref19
  doi: 10.1017/CBO9780511841552
– volume-title: Digital Image Processing
  year: 2017
  ident: ref28
– ident: ref5
  doi: 10.1016/bs.agph.2019.05.001
– ident: ref21
  doi: 10.1111/j.1365-2478.1992.tb00552.x
– ident: ref35
  doi: 10.1109/JSTSP.2012.2207371
– ident: ref6
  doi: 10.1109/ACCESS.2019.2938264
– ident: ref13
  doi: 10.1109/TGRS.2015.2462727
– ident: ref1
  doi: 10.1038/s41467-020-18361-4
– ident: ref32
  doi: 10.1029/138gm10
– ident: ref31
  doi: 10.1130/B30149.1
– ident: ref17
  doi: 10.1016/j.cageo.2013.04.012
– ident: ref24
  doi: 10.1080/2151237X.2007.10129236
– ident: ref4
  doi: 10.1007/978-3-642-13835-5
– ident: ref12
  doi: 10.1109/TGRS.2009.2012701
– ident: ref15
  doi: 10.1109/LGRS.2013.2248119
– ident: ref22
  doi: 10.1111/j.1365-2478.1985.tb00778.x
– volume-title: Methods for Non-Linear Least Squares Problems
  year: 2004
  ident: ref29
– ident: ref18
  doi: 10.1016/j.sigpro.2003.12.010
– ident: ref20
  doi: 10.1029/2019EA000680
– ident: ref10
  doi: 10.1109/TGRS.2016.2592679
– ident: ref33
  doi: 10.1029/2008GC002312
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Snippet Subbottom profilers (SBPs) using shipboard sonar can acquire massive amounts of data during exploration missions. Some geological intrusions and artificially...
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SubjectTerms Algorithms
Automation
Clustering
Curve fitting
Data acquisition
Fitting
Geoscience and remote sensing
Hyperbola-shaped signature
Hyperbolas
Image acquisition
Image processing
Image resolution
Image segmentation
morphological processing
Morphology
Noise
Profilers
Shape
Signatures
Sonar
Sonar detection
subbottom profiler (SBP)
Template matching
Title Automated Detection of Hyperbola-Shaped Signature in Subbottom Profiler Sonar Image With Morphological Processing
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