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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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 62; pp. 1 - 14
Main Authors: Chen, Pengcheng, Lu, Shaoping, Cai, Chen
Format: Journal Article
Language:English
Published: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Description
Summary: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.
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content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3443412