Side scan sonar image resolution and automatic object detection, classification and identification

Langner, Florian; Knauer, Christian; Jans, W.; Ebert, A.

doi:10.1109/oceanse.2009.5278183

Cited by 28 publications

(9 citation statements)

References 2 publications

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“…Reed et al (2003) extracted features based on the frequency domain of images to complete the recognition of sea mines from a sand sea bottom image [10]. Langner et al (2009) used a probabilistic neural network (PNN) to recognize underwater mine-like targets [11]. Isaacs (2015) found that features were generated on geometric moments in the imaging domain and performed a recognition process using an Ada-boosted decision tree classifier for underwater unexploded ordnance (UXO) remediation [12].…”

Section: Introductionmentioning

confidence: 99%

“…There are many kinds of feature extraction methods and recognition models used for SSS image target recognition that can achieve high correct recognition rate for certain different targets, with most research aimed at mine-like objects. All of these methods have been used to analyze SSS sample images [10][11][12][13][14][15][16][17], although not for the SSS whole waterfall image. What happens when combining some of these features as a comprehensive dataset for target recognition remains unclear.…”

Section: Introductionmentioning

confidence: 99%

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Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

et al. 2019

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In order to realize the automatic and accurate recognition of shipwreck targets in side-scan sonar (SSS) waterfall images, a pipeline that contains feature extraction, selection, and shipwreck recognition, an AdaBoost model was constructed by sample images. Shipwreck targets are detected quickly by a nonlinear matching model, and a shipwreck recognition in SSS waterfall images are given, and according to a wide set of combinations of different types of these individual procedures, the model is able to recognize the shipwrecks accurately. Firstly, two feature-extraction methods suitable for recognizing SSS shipwreck targets from natural sea bottom images were studied. In addition to these two typical features, some commonly used features were extracted and combined as comprehensive features to characterize shipwrecks from various feature spaces. Based on Independent Component Analysis (ICA), the preferred features were selected from the comprehensive features, which avoid dimension disaster and improved the correct recognition rate. Then, the Gentle AdaBoost algorithm was studied and used for constructing the shipwreck target recognition model using sample images. Finally, a shipwreck target recognition process for the SSS waterfall image was given, and the process contains shipwreck target fast detection by a nonlinear matching model and accurate recognition by the Gentle AdaBoost recognition model. The results show that the correct recognition rate of the model for the sample image is 97.44%, while the false positive rate is 3.13% and the missing detection rate is 0. This study of a measured SSS waterfall image confirms the correctness of the recognition process and model.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

et al. 2019

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“…Much of the earlier research uses a model based approach with knowledge of the target, focusing on highlights and shadows created by the strong reflection and occlusion of the protruding MLO from the seafloor [1]- [3]. In some research, the model for various range regions is used as a matched filter, convolving the filter with the image to detect regions of interest [4].…”

Section: Introductionmentioning

confidence: 99%

A Brain–Computer Interface (BCI) for the Detection of Mine-Like Objects in Sidescan Sonar Imagery

Barngrover

Althoff

DeGuzman³

et al. 2016

IEEE J. Oceanic Eng.

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Abstract-Detection of mine-like objects (MLOs) in sidescan sonar imagery is a problem that affects our military in terms of safety and cost. The current process involves large amounts of time for subject matter experts to analyze sonar images searching for MLOs. The automation of the detection process has been heavily researched over the years and some of these computer vision approaches have improved dramatically, providing substantial processing speed benefits. However, the human visual system has an unmatched ability to recognize objects of interest. This paper posits a brain-computer interface (BCI) approach, that combines the complementary benefits of computer vision and human vision. The first stage of the BCI, a Haar-like feature classifier, is cascaded in to the second stage, rapid serial visual presentation (RSVP) of images chips. The RSVP paradigm maximizes throughput while allowing an electroencephalography (EEG) interest classifier to determine the human subjects' recognition of objects. In an additional proposed BCI system we add a third stage that uses a trained support vector machine (SVM) based on the Haar-like features of stage one and the EEG interest scores of stage two. We characterize and show performance improvements for subsets of these BCI systems over the computer vision and human vision capabilities alone.Index Terms-Boosting, brain-computer interface (BCI), minelike object (MLO), object detection, rapid serial visual presentation (RSVP), sidescan sonar.

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“…This work concerns on design and simulation SSS, which is used for bathymetric surveys, commonly called seafloor mapping, by almost all navies throughout the world and also for other naval operations (see [1]- [4]). We made an attempt to establish a logical and systematic set of procedures that will help in design and analyze the performance of a SSS in order to satisfy different operational requirements.…”

Section: Introductionmentioning

confidence: 99%

DESIGN OF 100 kHz SONAR FOR MARINE MAPPING APPLICATIONS

Sathishkumar¹,

Gupta²,

Babu³

et al. 2013

Int. J. of Pure and Appl. Math.

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Side scan sonar (SSS) is an important mapping tool in underwear imaging system. This paper provides sufficient information on SSS design. SSS is known for very high quality, high resolution, ocean bottom imaging. It is able to rapidly survey large ocean areas for bottom and suspended sea-mines or other kinds of threats. Another operational aspect of these systems is that they allow autonomous underwater vehicles (AUVs) to conduct operations, mostly in shallow water and near land. It produces a representation in image form of the seabed; however, no closed form analytic expression or direct modeling technique exists to represent this complex process. In this paper the basic principle is presented for a model for the simulation of SSS, which produces as its output a qualitative image of the form generated by the actual SSS. The model considers the underlying physical processes including the effects of the refractive water medium, the scattering from the seabed and the transducer motion and directivity characteristics. Further, this work concerns the design and analysis of SSS via MATLAB simulation in order to satisfy the operational requirements. Moreover, this research has tried to indicate the various problems that may arise when a SSS operates in its near-field region and suggests certain solutions. The active sonar equation and its factors were explained and were evaluated.

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Side scan sonar image resolution and automatic object detection, classification and identification

Cited by 28 publications

References 2 publications

Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

A Brain–Computer Interface (BCI) for the Detection of Mine-Like Objects in Sidescan Sonar Imagery

DESIGN OF 100 kHz SONAR FOR MARINE MAPPING APPLICATIONS

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