Image Processing Techniques For the Detection and Classification of Man Made Objects in Side-Scan Sonar Images

Durá, Estrella

doi:10.5772/21920

Cited by 8 publications

(9 citation statements)

References 27 publications

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“…The optimal threshold calculated by our methods may be used to guide parametric popular edge-detection filters towards the best results. The resulting black and white images can feed a target recognition system which could apply machine learning methods in order to automatically classify the objects [4,43,44].…”

Section: Discussionmentioning

confidence: 99%

“…The track line under the ship carrying the sonar, the acoustic shadow generated by the beam, as well as many other factors (listed below), impair target detection [4].…”

Section: How Side-scan Sonars Workmentioning

confidence: 99%

“…Side-scan sonar is very popular in military applications such as Computer-Aided Detection (CAD) and Classification (CAC) of mines [10], reconnaissance, H-Bomb and shipwreck detection, etc. [4].…”

Section: Side-scan Sonar Applicationsmentioning

confidence: 99%

“…This research deals with the problem of the detection of man-made objects detected in side-scan sonar images. Man-made objects tend to have regular shapes which can be identified by obtaining their contour [4]. The most common process for separating an image into regions or their contours corresponding to objects is segmentation.…”

Section: Objective and Paper Organizationmentioning

confidence: 99%

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Contour Extraction Based on Adaptive Thresholding in Sonar Images

Andreatos

Leros

2021

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A common problem in underwater side-scan sonar images is the acoustic shadow generated by the beam. Apart from that, there are a number of reasons impairing image quality. In this paper, an innovative algorithm improving contour extraction is presented. Contour extraction is based on automatically estimating the optimal threshold for converting the original gray scale images into binary images. The proposed algorithm clears the shadows and masks most of the impairments in side-scan sonar images. The idea is to select a proper threshold towards the rightmost local minimum of the histogram, i.e., closest to the white values. For this purpose, the histogram envelope is approximated by two alternative contour extraction methods: polynomial curve fitting and data smoothing. Experimental results indicate that the proposed algorithm produces superior results than popular thresholding methods and common edge detection filters, even after corrosion expansion. The algorithm is simple, robust and adaptive and can be used in automatic target recognition, classification and storage in large-scale multimedia databases.

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Section: Discussionmentioning

confidence: 99%

“…The track line under the ship carrying the sonar, the acoustic shadow generated by the beam, as well as many other factors (listed below), impair target detection [4].…”

Section: How Side-scan Sonars Workmentioning

confidence: 99%

Section: Side-scan Sonar Applicationsmentioning

confidence: 99%

Section: Objective and Paper Organizationmentioning

confidence: 99%

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Contour Extraction Based on Adaptive Thresholding in Sonar Images

Andreatos

Leros

2021

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“…A recent focus topic has been side-scan sonar image clustering, segmentation, and classification. One of the most comprehensive literatures regarding sonar image processing for detection and classification of man-made objects can be found in (Dura, 2011). Many researchers have tried to adopt different methods of detection, segmentation and classification in sidescan sonar imaging (Nelson & Krylov, 2014;Buscombe et al, 2016;Vikas, 2017).…”

mentioning

confidence: 99%

Support Vector Machine and Decision Tree Based Classification of Side-Scan Sonar Mosaics Using Textural Features

Febriawan

Helmholz

Parnum

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The diversity and heterogeneity of coastal, estuarine and stream habitats has led to them becoming a prevalent topic for study. Woody ruins are areas of potential riverbed habitat, particularly for fish. Therefore, the mapping of those areas is of interest. However, due to the limited visibility in some river systems, satellites, airborne or other camera-based systems (passive systems) cannot be used. By contrast, sidescan sonar is a popular underwater acoustic imaging system that is capable of providing high- resolution monochromatic images of the seafloor and riverbeds. Although the study of sidescan sonar imaging using supervised classification has become a prominent research subject, the use of composite texture features in machine learning classification is still limited. This study describes an investigation of the use of texture analysis and feature extraction on side-scan sonar imagery in two supervised machine learning classifications: Support Vector Machine (SVM) and Decision Tree (DT). A combination of first- order texture and second-order texture is investigated to obtain the most appropriate texture features for the image classification. SVM, using linear and Gaussian kernels along with Decision Tree classifiers, was examined using selected texture features. The results of overall accuracy and kappa coefficient revealed that SVM using a linear kernel leads to a more promising result, with 77% overall accuracy and 0.62 kappa, than SVM using either a Gaussian kernel or Decision Tree (60% and 73% overall accuracy, and 0.39 and 0.59 kappa, respectively). However, this study has demonstrated that SVM using linear and Gaussian kernels as well as a Decision Tree makes it capable of being used in side-scan sonar image classification and riverbed habitat mapping.</p>

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