Towfish orientation and position estimation

The towfish location and orientation problems that arise in using side-scan sonar to detect objects on the sea bottom are treated separately. Data which locate the towfish relative to the ship are usually deteriorated by multipath receptions and other effects. In order to overcome this serious degradation in the location measurements, a modified Kalman filter is proposed. An estimate of the state transition matrix for this filter is derived, and a means of switching between two Kalman gains is suggested. The feasibility of the proposed filter is justified by a case study. Improved estimates of towfish pitch and heading measurements are obtained by a separate system employing model identification and subsequent Kalman filtering. Application of these methods to data from similar towed side-scan sonar systems should yield significant gains in object location accuracy     

Sea-bed characterisation and classification from the power spectra of side-scan sonar data

This work is concerned with the automatic characterisation and classification of the sea-bed for side-scan sonar trace power-spectra.A parametric model of side-scan sonar trace power-spectra is developed from the equation for the magnitude frequency response of a Butterworth filter. The model's parameters are optimised to give a least squares fit with observed spectra. Three of the optimised parameters are used to define features.     

High-resolution deep-tow survey imagery,Gulf of Mexico

Data are presented from a commercially available deep-tow 100-kHz side-scan sonar and 3.5-kHz subbottom profiler acquisition system. The towfish is a positively buoyant vehicle referenced to the seafloor with an anchor chain deadweight. This enables the acoustic sources and receivers to be towed at a constant altitude of 30 to 35 m above the seafloor. The records illustrate a capability for imaging deep-water seafloor morphology and near-surface sediments to water depths of 2200 m in the Gulf of Mexico.     

Active learning for detection of mine-like objects in side-scan sonar imagery

A data-adaptive algorithm is presented for the selection of the basis functions and training data used in classifier design with application to sensing mine-like targets with a side-scan sonar. Automatic detection of mine-like targets using side-scan sonar imagery is complicated by the variability of the target, clutter, and background signatures. Specifically, the strong dependence of the data on environmental conditions vitiates the assumption that one may perform a priori algorithm training using separate side-scan sonar data collected previously. In this paper, a novel active-learning algorithm is developed based on kernel classifiers with the goal of enhancing detection/classification of mines without requiring an a priori training set. It is assumed that divers and/or unmanned underwater vehicles (UUVs) may be used to determine the binary labels (target/clutter) of a small number of signatures from a given side-scan collection. These sets of signatures and associated labels are then used to train a kernel-based algorithm with which the remaining side-scan signatures are classified. Information-theoretic concepts are used to adaptively construct the form of the kernel classifier and to determine which signatures and associated labels would be most informative in the context of algorithm training. Using measured side-looking sonar data, the authors demonstrate that the number of signatures for which labels are required (via diver/UUV) is often small relative to the total number of potential targets in a given image. This procedure designs the detection/classification algorithm on the observed data itself without requiring a priori training data and also allows adaptation as environmental conditions change.     

联合小波和NLM滤波的侧扫声纳回波信号降噪

为进一步降低侧扫声纳回波信号中非高斯分布的乘性噪声,获取更佳效果的侧扫声纳图像,提出了一种利用小波和NLM(nonlocal means)滤波的组合降噪方法.首先采用同态变换将侧扫声纳回波ping信号中的乘性噪声转换为加性噪声,然后利用小波阈值和NLM滤波对侧扫声纳每ping回波数据实施降噪处理,最后经过小波反变换和指...     

基于数学形态学的侧扫声呐图像轮廓自动提取

侧扫声呐图像特征自动提取的难点在于特征地貌边缘检测较困难,依据图像灰度突变检测得到的边缘比较粗糙、不连续,而且有断口和小洞。本文在对图像进行预处理和阈值化的基础上,采用数学形态学方法对图像进行处理,即用具有一定形态的结构元素去量度和提取图像中的对应形状,得到连续化、粗化、圆滑的特征区域边缘填充目标内部阴影且消除背景噪声。基于数学形态学的侧扫声呐图像特征自动提取的主要步骤为:首先对侧扫声呐图像进行预处理,然后进行灰度阈值化,接着采用数学形态学方法进行处理,最后对处理后的图像进行边缘检测,提取出特征地貌边缘。实验表明,采用数学形态学方法进行处理后,错断、离散的海底目标物变得连续,背景噪声大大减少,自动提取结果准确可靠。     

基于虚警函数的侧扫声纳水下目标实时检测方法

研究了侧扫声纳系统进行水下目标探测过程中目标信号的检测问题。通过分析海底回波信号的统计模型及其参数的估计,讨论了目标信号对统计模型拟合的影响规律,提出了侧扫声纳回波信号虚警函数和虚警率的概念,及其对Ping信号中目标信号的检测方法。算例结果表明,回波信号的三种分布模型中K分布拟合程度最优,在相同虚警率的条件下,基于K分布的虚警函数目标检测率最高。该法可为侧扫声纳回波信号中目标的实时报警提供技术支撑。     

Methods of estimating allowable motion perturbations in side-scansonar systems

This document describes the effects of nonideal hydrophone array motion on sonar image quality and derives the allowable motion errors for vehicles carrying side-scan sonar systems. Estimates of the quantity of motion that causes just noticeable sensor performance degradation as well as those that render the sensors useless are derived using two different methods. Using a special resolution target, examples of simulated image distortion resulting from several types of motion are presented. A method for quantitatively measuring the actual image distortion in situ is described     

小波函数对侧扫声纳图像滤波效果的影响分析

侧扫声纳技术应用日益广泛,已成为海洋测量的重要工具,而去除噪声处理是对侧扫声纳图像进行正确判读的前提。利用小波函数滤波处理的方法,分别采用Haar、Daubechies、Coiflets、Symlets、Discrete Meyer、Biorthogonal、Reverse Biorthogonal等小波函数与中值滤波函数对侧扫声纳图像进行处理,并以平滑指数和边缘保持指数为评价指标,对滤波效果进行定量比较。试验表明,小波函数可以有效地平滑声纳图像,并能保持其较好的边缘效果。     

Fault scarp identification in side-scan sonar and bathymetry images from the Mid-Atlantic Ridge using wavelet-based digital filters

Digital filters designed using wavelet theory are applied to high resolution deep-towed side-scan sonar data from the median valley walls, crestal mountains, and flanks of the Mid-Atlantic Ridge at 29°10 N. With proper tuning, the digital filters are able to identify the location, orientation, length, and width of highly reflective linear features in sonar images. These features are presumed to represent the acoustic backscatter from axis-facing normal faults. The fault locations obtained from the digital filters are well correlated with visual geologic interpretation of the images. The side-scan sonar images are also compared with swath bathymetry from the same area. The digitally filtered bathymetry images contain nine of the eleven faults identified by eye in the detailed geologic interpretation of the side-scan data. Faults with widths (measured perpendicular to their strike) of less than about 150 m are missed in the bathymetry analysis due to the coarser resolution of these data. This digital image processing technique demonstrates the potential of wavelet-based analysis to reduce subjectivity and labor involved in mapping and analyzing topographic features in side-scan sonar and bathymetric image data.     

Side-scan sonar image matching

This paper presents a method for the matching of underwater images acquired with acoustic sensors. As a final objective, the system aims at matching data from two-dimensional scenes. The proposed approach carries out a hypothetical reasoning based on objects, represented by shadows and echoes in the sonar images, and their available features. The problem of determining measures which are invariant to changes in sonar settings and noise characteristics is addressed by mapping robust features for sonar images to a qualitative representation. To cope with the viewpoint charging appearance, the method is based on the conservation of objects' relative position from one image to another. We attempt to match geometrical structures formed by the association of three objects. The hypothetical reasoning is conducted in a decision tree framework. A tree node is generated by two objects' association, each one belonging to a respective image. Hypotheses propagation consists of creating new nodes from neighboring associations. The matching solution is determined by the selection of the decision tree's longest branch. Thus, the association mechanism is a depth-first procedure. The proposed method has been applied to real high-resolution side-scan sonar images. The matching process has shown successful and promising results which have been further improved. In particular, the parceled shadows (during the segmentation procedure) problem has been tackled     

Automatic Registration of TOBI Side-Scan Sonar and Multi-Beam Bathymetry Images for Improved Data Fusion

Deep towed side-scan sonar vehicles such as TOBI acquire high quality imagery of the seafloor with very high spatial resolution but poor locational accuracy. Fusion of the side-scan sonar data with bathymetry data from an independent source is often desirable to reduce ambiguity in geological interpretations, to aid in slant-range correction and to enhance seafloor representation. The main obstacle to fusion is accurate registration of the two datasets.The application of hierarchical chamfer matching to the registration of TOBI side-scan sonar images and multi-beam swath bathymetry is described. This matches low level features such as edges in the TOBI image, with corresponding features in a synthetic TOBI image created by simulating the flight of the TOBI vehicle through the bathymetry. The method is completely automatic, relatively fast and robust, and much easier than manual registration. It allows accurate positioning of the TOBI vehicle, enhancing its usefulness as a research tool. The method is illustrated by automatic registration of TOBI and multi-beam bathymetry data from the Mid-Atlantic Ridge.     

一种基于MATLAB的声呐条带图像自动拼接算法

准确地实现侧扫声呐条带的拼接对于了解海底地形、提高对海床地物反映的准确度起着重要的作用,而相邻条带的配准是声呐条带拼接的重要前提.MATLAB以其强大的矩阵运算功能及具有丰富的图像处理函数等特点,在图像处理方面占据明显的优势.文中利用MATLAB的IPT工具箱实现了基于互信息方法的声呐条带图像的自动快速配准,通过实验验证了该配准方法的有效性.并用小波变换方法对配准好的条带图像进行融合,实现声呐条带图像的有效拼接和镶嵌.     

声纳记录失真分析

从侧扫声纳的工作原理出发，分析了侧扫声纳记录声图产生基本失真的原因，推导出基本失真的数学公式，所得结论对实际作用有重要的指导意义。     

Curvelet变换域侧扫声纳图像增强算法

针对海底侧扫声纳图像对比度低、纹理弱、噪声严重等问题,提出了一种基于第二代Curvelet变换的声纳图像增强算法。首先对原始声纳图像进行多尺度、多方向的Curvelet变换分解,得到低频子带和高频子带;然后引入非线性S型函数对低频系数进行处理,提高图像整体的对比度;采用一种可以避免过度增强的新型非线性函数对各尺度的高频子带系数进行处理,提高图像整体的对比度,增强图像边缘和纹理细节,并通过估计噪声水平设定阈值进行阈值降噪。最后经Curvelet逆变换得到增强图像。实验表明,该方法不仅改善了海底侧扫声纳图像对比度低的问题,而且降低了噪声,突出了声纳图像的边缘和纹理细节。     

侧扫声纳图像增强技术

随着海洋探测技术的飞速发展,侧扫声纳的图像质量和定位精度迅速提高,其应用日益广泛和深入。在声纳图像判读分析时,常常需要进行辐射改正和均衡增强。根据海底地貌声纳图像的特点,归纳了几种有效、适用的图像增强方法,并建立了声纳图像分析软件。     

GeoSwath与Klein 3000的综合对比及实验分析

GeoSwath Plus系统利用相干原理能同时得到水深数据和声纳图,与传统侧扫声纳存在一定差异。本文对GeoSwath Plus 125 kHZ系统和Klein 3000系统从工作原理、技术参数、数据采集和后处理平台以及实测数据等方面进行了对比。结果表明:二者接收数据存在差异,导致两者的用途也存在差异;GeoSwath Plus系统相比Klein 3000更加复杂,但适用水深范围较小;GeoSwath Plus采集软件较少,对后处理软件的要求更高,而Klein 3000数据采集和后处理软件都有多种选择;通过实测数据的比较发现,GeoSwath Plus的定位精度更高,但由于发射波束水平开角大于Klein 3000,加之采用悬挂式安装,导致得到的声纳图像对比度和分辨率比传统侧扫声纳Klein 3000要差一些。     

侧扫声纳探测技术的研究现状及发展

侧扫声纳是海洋地形地貌测量的必备仪器之一。主要介绍了侧扫声纳探测技术的研究现状,重点阐述了其最新研究进展,给出了侧扫声纳技术未来的发展趋势。针对国内外实际应用需求,提出了几点建议,以期为我国侧扫声纳技术相关研究提供参考。     

基于侧扫声纳图像海底目标物提取方法

分析了侧扫声纳数据成像原理,利用凹凸目标成像与阴影的关系,依次采用二值化处理、中值滤波处理、开启与闭合操作、Canny边缘提取等多种图像处理的手段,实现了海底目标物的提取与定位,并通过实验验证了本方法的可行性。