A Side Scan Sonar Image Target Detection Algorithm Based on a Neutrosophic Set and Diffusion Maps PDF Download
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Author: Xiao Wang
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 16
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Book Description
To accurately achieve side scan sonar (SSS) image target detection, a novel target detection algorithm based on a neutrosophic set (NS) and diffusion maps (DMs) is proposed in this paper.
Author: Xiao Wang
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 16
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Book Description
To accurately achieve side scan sonar (SSS) image target detection, a novel target detection algorithm based on a neutrosophic set (NS) and diffusion maps (DMs) is proposed in this paper.
Author: James Derek Tucker
Publisher:
ISBN:
Category : Acoustic imaging
Languages : en
Pages : 244
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Book Description
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The effect of bottom backscatter on target detection ranges for 100- kHz Klein and EG & G side scan sonars was investigated. Glass spheres of 16-cm diameter with measured target strengths of -24 dB were deployed in 30-m water depth, 0.7 m above sand and shale bottoms. Controlled test runs past a linear target configuration were performed. For a sand bottom, the Klein system yielded target detections at a maximum range of 150 m with 100% success. The EG & G system yielded 100% detection out to 152-m range, with detection 46% of the time at 259 m and 86% at 228 m. A shale bottom masked all target returns negating detection. Detection thresholds were estimated by comparing field results to theoretical ranges calculated from the sonar equation using applicable backscatter coefficients. The results show that it is possible to determine the geophysical and side scan system inputs sufficiently well to allow determination of the efficient spacing of survey lines in shallow water hydrographic applications of side scan sonar.
Author: Maureen R. Kenny
Publisher:
ISBN:
Category :
Languages : en
Pages : 135
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Book Description
The effect of bottom backscatter on target detection ranges for 100- kHz Klein and EG & G side scan sonars was investigated. Glass spheres of 16-cm diameter with measured target strengths of -24 dB were deployed in 30-m water depth, 0.7 m above sand and shale bottoms. Controlled test runs past a linear target configuration were performed. For a sand bottom, the Klein system yielded target detections at a maximum range of 150 m with 100% success. The EG & G system yielded 100% detection out to 152-m range, with detection 46% of the time at 259 m and 86% at 228 m. A shale bottom masked all target returns negating detection. Detection thresholds were estimated by comparing field results to theoretical ranges calculated from the sonar equation using applicable backscatter coefficients. The results show that it is possible to determine the geophysical and side scan system inputs sufficiently well to allow determination of the efficient spacing of survey lines in shallow water hydrographic applications of side scan sonar.
Author: Timothy Pohajdak
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Autonomous underwater vehicles (AUVs) are frequently used to survey sea-floor environments using side-scan sonar technology. A simultaneous localization and mapping (SLAM) algorithm can be used with side-scan sonar data gathered during surveying to bound the possible error in AUV position estimate, and increase overall position accuracy, using only information already gathered during the survey mission. One problem in using SLAM to improve localization is that data from a preliminary or route survey on the sea floor may be inaccurate due to changes in the sea bed or merely be differently detected due to different side-scan sonar surveying patterns or equipment. This thesis' focus is an integrated on-board SLAM system using automated target recognition system to extract objects for SLAM data association, data association algorithms for MLOs (joint compatibility program), and finally change detection on the SLAM results to determine if new objects have been introduced to the sea floor.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 29
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Book Description
This paper investigates the detection of possible targets in sidescan sonar images using two-dimensional convolutions of filters with the sidescan image. The filters are designed to reflect the highlight/shadow features of targets. A high convolution value indicates a possible target. Two data sets, one from the SQS-511 sonar and one from a Klein 5000 sonar towed by an autonomous vehicle, are analyzed. The results indicate whether this method may provide a robust method for automated target detection.
Author: David C. Grimm
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 119
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Book Description
"Detection of a known target in an image has several different approaches. The complexity and number of steps involved in the target detection process makes a comparison of the different possible algorithm chains desirable. Of the different setps involved, some have a more significant impact than others on the final result - the ability to find a target in an image. These more important steps often include atmospheric compensation, noise and dimensionality reduction, background characterization, and detection (matched filtering for this research). A brief overview of the algorithms to be compared for each step will be presented. This research seeks to identify the most effective set of algorithms for detecting a known target. Several different algorithms for each step will be presented, to include ELM, FLAASH, ACORN, MNF, PPI, N-FINDR, MAXD, and two matched filters that employ a structured background model - OSP and ASD. The chains generated by these algorithms will be compared using the Forest Radiance I HYDICE data set. Finally, ROC curves and AFAR values are calculated for each algorithm chain and a comparison of them is presented. Detection rates at a CFAR are also compared. Since a relatively small number of algorithms were used for each step, there were no definitive results generated. However, a comprehensive comparison of the chains using the above mentioned algorithms is presented"--Abstract.
Author: Nikolai Kolev
Publisher: IntechOpen
ISBN: 9789533073453
Category : Technology & Engineering
Languages : en
Pages : 336
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Book Description
The book is an edited collection of research articles covering the current state of sonar systems, the signal processing methods and their applications prepared by experts in the field. The first section is dedicated to the theory and applications of innovative synthetic aperture, interferometric, multistatic sonars and modeling and simulation. Special section in the book is dedicated to sonar signal processing methods covering: passive sonar array beamforming, direction of arrival estimation, signal detection and classification using DEMON and LOFAR principles, adaptive matched field signal processing. The image processing techniques include: image denoising, detection and classification of artificial mine like objects and application of hidden Markov model and artificial neural networks for signal classification. The biology applications include the analysis of biosonar capabilities and underwater sound influence on human hearing. The marine science applications include fish species target strength modeling, identification and discrimination from bottom scattering and pelagic biomass neural network estimation methods. Marine geology has place in the book with geomorphological parameters estimation from side scan sonar images. The book will be interesting not only for specialists in the area but also for readers as a guide in sonar systems principles of operation, signal processing methods and marine applications.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 22
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Book Description
Side-scan sonars deployed on towfish are a valued tool for imaging the sea bottom. This paper describes some recent observations in the practical effects of sidelobes and the effects of towfish attitude on the image. The images analyzed are of corner-cube reflectors, which give a very strong reflection visible over a wide range of angles. The internal structure of this reflection can be studied to investigate the beam pattern. It was also possible, in this case, to see an effect of towfish yaw.
Author: Leidy P. Dorado-Munoz
Publisher:
ISBN:
Category : Multispectral imaging
Languages : en
Pages : 262
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Book Description
"Applications of optical remote sensing processes include environmental monitoring, military monitoring, meteorology, mapping, surveillance, etc. Many of these tasks include the detection of specific objects or materials, usually few or small, which are surrounded by other materials that clutter the scene and hide the relevant information. This target detection process has been boosted lately by the use of hyperspectral imagery (HSI) since its high spectral dimension provides more detailed spectral information that is desirable in data exploitation. Typical spectral target detectors rely on statistical or geometric models to characterize the spectral variability of the data. However, in many cases these parametric models do not fit well HSI data that impacts the detection performance. On the other hand, non-linear transformation methods, mainly based on manifold learning algorithms, have shown a potential use in HSI transformation, dimensionality reduction and classification. In target detection, non-linear transformation algorithms are used as preprocessing techniques that transform the data to a more suitable lower dimensional space, where the statistical or geometric detectors are applied. One of these non-linear manifold methods is the Schroedinger Eigenmaps (SE) algorithm that has been introduced as a technique for semi-supervised classification. The core tool of the SE algorithm is the Schroedinger operator that includes a potential term that encodes prior information about the materials present in a scene, and enables the embedding to be steered in some convenient directions in order to cluster similar pixels together. A completely novel target detection methodology based on SE algorithm is proposed for the first time in this thesis. The proposed methodology does not just include the transformation of the data to a lower dimensional space but also includes the definition of a detector that capitalizes on the theory behind SE. The fact that target pixels and those similar pixels are clustered in a predictable region of the low-dimensional representation is used to define a decision rule that allows one to identify target pixels over the rest of pixels in a given image. In addition, a knowledge propagation scheme is used to combine spectral and spatial information as a means to propagate the 'potential constraints' to nearby points. The propagation scheme is introduced to reinforce weak connections and improve the separability between most of the target pixels and the background. Experiments using different HSI data sets are carried out in order to test the proposed methodology. The assessment is performed from a quantitative and qualitative point of view, and by comparing the SE-based methodology against two other detection methodologies that use linear/non-linear algorithms as transformations and the well-known Adaptive Coherence/Cosine Estimator (ACE) detector. Overall results show that the SE-based detector outperforms the other two detection methodologies, which indicates the usefulness of the SE transformation in spectral target detection problems."--Abstract.
