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Preliminary Results Of An Algorithm For Automatic Detection Of Mine Like Objects In Sidescan Sonar Images
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Book Synopsis Preliminary Results of an Algorithm for Automatic Detection of Mine-like Objects in Sidescan Sonar Images by :
Download or read book Preliminary Results of an Algorithm for Automatic Detection of Mine-like Objects in Sidescan Sonar Images written by and published by . This book was released on 2000 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Automated Detection of Mine-Like Objects in Side Scan Sonar Imagery by : Christopher M. Barngrover
Download or read book Automated Detection of Mine-Like Objects in Side Scan Sonar Imagery written by Christopher M. Barngrover and published by . This book was released on 2014 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: The task of detecting mine-like objects (MLOs) in side scan sonar imagery has a profound impact on military operations. The current process involves subject matter experts analyzing sonar images searching for MLOs. The automation of this problem has been heavily researched over the years without a definitive solution that outperforms the manual approach in real world scenarios. This paper presents a series of approaches and experiments centered on the use of GentleBoost feature selection classifiers for the detection of MLOs in side scan sonar. In a comparison of semi-synthetic versus real world training data with two different boosted single-feature selection classifiers, we see that semi-synthetic data can provide insight in to potential performance of a classifier. We run experiments training and testing GentleBoost single-feature classifiers on six different feature types, finding that the Haar-like feature classifier performs the best. We propose a GentleBoost multi-feature selection framework that allows for multiple feature types to be in the pool of selectable features, finding that a combination of Haar-like features, speeded up robust features (SURF), and simple shadow features performs better than the Haar-like feature classifier. Experiments with tiered, or cascaded, classifiers show a reduction in false positives for lower true positive rates. The multiple instance learning (MIL) approach shows great potential for future efforts, achieving improved true positive rates at higher false positive rates. A final approach considers the complimentary benefits of computer vision and human vision, introducing two brain-computer interface (BCI) systems. One BCI uses the Haar-like feature classifier as a first stage cascaded in to a human processing second stage. The other adds a third stage that employs a novel support vector machine (SVM) classifier based on the Haar-like feature and human interest scores from multiple subjects. Overall, our GentleBoost feature selection classifier variations result in performance improvement for the detection of MLOs in side scan sonar imagery.
Book Synopsis Algorithms and Data Structures for Automated Change Detection and Classification of Sidescan Sonar Imagery by :
Download or read book Algorithms and Data Structures for Automated Change Detection and Classification of Sidescan Sonar Imagery written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: During Mine Warfare (MIW) operations, MIW analysts perform change detection by visually comparing historical sidescan sonar imagery (SSI) collected by a sidescan sonar with recently collected SSI in an attempt to identify objects (which might be explosive mines) placed at sea since the last time the area was surveyed. This dissertation presents a data structure and three algorithms, developed by the author, that are part of an automated change detection and classification (ACDC) system. MIW analysts at the Naval Oceanographic Office, to reduce the amount of time to perform change detection, are currently using ACDC. The dissertation introductory chapter gives background information on change detection, ACDC, and describes how SSI is produced from raw sonar data. Chapter 2 presents the author's Geospatial Bitmap (GB) data structure, which is capable of storing information geographically and is utilized by the three algorithms. This chapter shows that a GB data structure used in a polygon-smoothing algorithm ran between 1.3 - 48.4x faster than a sparse matrix data structure. Chapter 3 describes the GB clustering algorithm, which is the author's repeatable, order-independent method for clustering. Results from tests performed in this chapter show that the time to cluster a set of points is not affected by the distribution or the order of the points. In Chapter 4, the author presents his real-time computer-aided detection (CAD) algorithm that automatically detects mine-like objects on the seafloor in SSI. The author ran his GB-based CAD algorithm on real SSI data, and results of these tests indicate that his real-time CAD algorithm performs comparably to or better than other non-real-time CAD algorithms. The author presents his computer-aided search (CAS) algorithm in Chapter 5. CAS helps MIW analysts locate mine-like features that are geospatially close to previously detected features. A comparison between the CAS and a great circle distance algorithm shows that the CAS performs geospatial searching 1.75x faster on large data sets. Finally, the concluding chapter of this dissertation gives important details on how the completed ACDC system will function, and discusses the author's future research to develop additional algorithms and data structures for ACDC.
Book Synopsis Detection and Remediation Technologies for Mines and Minelike Targets by :
Download or read book Detection and Remediation Technologies for Mines and Minelike Targets written by and published by . This book was released on 1998 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Trainable Linear Filters for the Automated Detection of Mines in Side Scan Sonar Images by :
Download or read book Trainable Linear Filters for the Automated Detection of Mines in Side Scan Sonar Images written by and published by . This book was released on 1999 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: The multibeam side-scan sonar images used for sea mine hunting look like aerial photographs of the seafloor, but there are important differences. The sonar's automatic gain control, the consistent highlight-shadow signature progression of objects, and the absence of vanishing points considerably reduce the degrees of freedom in an object's appearance, making sonar images better suited for linear detection schemes than optical photographs generally are. This paper demonstrates a trainable linear filter for the detection of mine-like objects in side-scan sonar images. The training is fast & flexible, using actual or synthetic sonar images to prescribe what image patterns are to be detected & rejected, and with a sliding filter window of any shape to focus more particularly on important features. Measures of performance such as the probabilities of detection & false alarm, and the limits of trained discrimination and window size, are derived. The method is demonstrated for mine-like targets in actual sonar images.
Book Synopsis A Side Scan Sonar Image Target Detection Algorithm Based on a Neutrosophic Set and Diffusion Maps by : Xiao Wang
Download or read book A Side Scan Sonar Image Target Detection Algorithm Based on a Neutrosophic Set and Diffusion Maps written by Xiao Wang and published by Infinite Study. This book was released on with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Identifying Features that Distinguish Between Mine-like Objects in Sidescan Sonar Imagery by : Patrick C. Connor
Download or read book Identifying Features that Distinguish Between Mine-like Objects in Sidescan Sonar Imagery written by Patrick C. Connor and published by . This book was released on 2004 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Roughness Estimation Algorithm for Sidescan by :
Download or read book A Roughness Estimation Algorithm for Sidescan written by and published by . This book was released on 2007 with total page 3 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Naval Oceanographic Office (NAVOCEANO) requires accurate estimates of seafloor roughness (bottom relief) and the density of seafloor clutter (mine-like echoes), typically derived from sidescan sonar imagery (SSI), to determine the bottom type of a geographic area for mine warfare. Determining clutter and roughness manually can be time-consuming and produce inconsistent results. Automated algorithms can derive clutter and roughness from SSI in a consistent and timely manner. Features such as pockmarks, sand ripples, and rocks on the seafloor are visible in SSI as bright spots ("brights") with adjacent shadows. The Naval Research Laboratory (NRL) developed a real-time clutter detection algorithm (transitioned to NAVOCEANO in 2001) that quickly and reliably identifies clutter in SSI and clusters the results into polygons. An object's height (estimated from the length of its shadow) is one measurement used to determine whether the object is mine-like. The authors theorized that height also could be used to automatically estimate seafloor roughness. NRL has developed a new automated roughness estimation algorithm, based on the clutter detection algorithm, to automatically derive seafloor roughness from SSI. In repeated trials, polygons generated by the new roughness algorithm correlated well (as high as 87%) with manually generated polygons for the same region. This article presents the NRL automated roughness algorithm (transitioned to NAVOCEANO in 2006), including test results and comparisons with manual methods.
Book Synopsis Computational and Physical Experiments on Autonomous Underwater Object Detection with Low-cost Imaging Sonar and Computer Graphics Simulations by : Ruoyao Qin
Download or read book Computational and Physical Experiments on Autonomous Underwater Object Detection with Low-cost Imaging Sonar and Computer Graphics Simulations written by Ruoyao Qin and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: detection algorithm is implemented using the synthetic sonar images generated from a computer graphics tool in Unreal Engine. The trained object detection algorithm is tested with realistic sonar images that are generated from a physics-based simulation in order to analyze the transfer learning performance. In addition, the presented object detection algorithm is also tested with real side-scan sonar images obtained from a low-cost fish finder, which is equipped on a sonar boat being towed by a drone. The experimental results show that various pre-processing processes can improve transfer learning performance significantly.
Download or read book Oceans 2001 MTS/IEEE written by and published by . This book was released on 2001 with total page 748 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Development of an Automated Roughness Estimation Algorithm for Sidescan Imagery by :
Download or read book Development of an Automated Roughness Estimation Algorithm for Sidescan Imagery written by and published by . This book was released on 2007 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimations of seafloor clutter (also called mine-like echoes) and roughness (bottom relief) are needed during mine warfare operations to determine seafloor type for a geographic region of interest. Analysts at the Naval Oceanographic Office estimate bottom clutter and roughness from sidescan sonar imagery (SSI). However, detecting clutter and determining roughness manually is often time consuming and can produce inconsistent results. The Naval Research Laboratory has developed an algorithm to automatically derive seafloor roughness from SSI. In repeated trials, results of the automated roughness algorithm correlated well (as high as 87%) with manual roughness estimations.
Book Synopsis A Probabilistic Theory for the Design of Optimal Linear Discriminators of the Automated Detection of Objects in Sidescan Sonar Images by :
Download or read book A Probabilistic Theory for the Design of Optimal Linear Discriminators of the Automated Detection of Objects in Sidescan Sonar Images written by and published by . This book was released on 2000 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computerized pattern recognition can be used to help a sonar operator locate underwater objects in sidescan sonar images. This report presents the theory behind several linear discriminators, with a view to improving automated detection. The discriminators are formulated mathematically to be optimal insofar as they maximize the detection performance as defined under the Neyman-Pearson design criteria, with adjustments made to those criteria to suit the prior knowledge of both the objects sought & the local seafloor clutter. The emphasis throughout is on sea mine detection in naval operations. The theory gives practical insight & direction for the mine detection problem, showing, for instance, what kind of data should be extracted from target & clutter image libraries to get optimal detection performance; the possibility of there being several optimal linear discriminators in the case when nothing is assumed about the local seafloor clutter; and the effect of prior de-meaning of images on detection performance.
Download or read book Oceans 2005, Europe written by and published by Institute of Electrical & Electronics Engineers(IEEE). This book was released on 2005 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Sonar Systems written by Nikolai Kolev and published by IntechOpen. This book was released on 2011-09-12 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Image-based Classification of Sidescan Sonar Detections by :
Download or read book Image-based Classification of Sidescan Sonar Detections written by and published by . This book was released on 2001 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ability to classify sonar detections in mine countermeasures as manmade or not manmade is very important. Once a detection is made on a sidescan sonar image, a small image containing the detection is typically extracted, then segmented into highlight, shadow, & background regions, and various features of the highlight & shadow are computed for input into a classification algorithm. However, image segmentation can be a difficult process, especially for complex seabed backgrounds, and any resulting classification is only as good as the previous segmentation & feature extraction algorithms. This report examines the possibility of using the small image itself as the feature for classification, without any segmentation & feature extraction. Experiments were conducted using a data set of three classes of synthetic images. Principal component analysis was followed by discriminate analysis to obtain vectors used to cluster the images which were then classified using simple methods. Another approach tested was to input the images to the commercial software package HNET. The results show whether it is possible to obtain a good classifier based on imagery alone.
Book Synopsis Computer-aided Classification for a Database of Images of Minelike Objects by :
Download or read book Computer-aided Classification for a Database of Images of Minelike Objects written by and published by . This book was released on 2005 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report describes some computer classification experiments with a database containing side-scan sonar images with a large variety of objects and sea bed backgrounds. The database consists of 383 swaths of sonar data extracted from sea trial data collected over the last few years using a Klein 5500 side-scan sonar. The effects of the filtering & image segmentation processes on the resultant classification rates are considered. A number of kernel-based & nearest-neighbour classification schemes are examined. The results presented indicate whether high classification rates & low false alarm rates can be achieved despite the complexities of the database studied.
Download or read book Maritime Information Review written by and published by . This book was released on 1993 with total page 1080 pages. Available in PDF, EPUB and Kindle. Book excerpt:
