共查询到17条相似文献，搜索用时 125 毫秒
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侧扫声纳是海洋测绘领域的常用设备,为解决侧扫声纳波束模式带来的声纳图像中央区域质量较差的问题,提出了侧扫声纳图像中央区的自动确定和重建方法。首先根据侧扫声纳测量原理,基于波束模式,自动确定侧扫声纳图像的中央区域;然后根据图像强度梯度和像素可靠信息,计算图像重建区域的优先级;最后根据优先级顺序,采用基于样例的方法对中央区进行重建,提高侧扫声纳图像质量。研究表明,重建后的侧扫声纳图像无论是在主观视觉还是客观评价指标方面,都取得了满意的结果。采用本文所提方法得到的重建图像,能够清晰地反映海底特征,具有很好的应用价值。 相似文献
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小波函数对侧扫声纳图像滤波效果的影响分析 总被引：1，自引：0，他引：1
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。 相似文献
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船载低频多波束测深声纳、侧扫声纳可以对深海海底地形地貌进行快速、高效、大面积探测,但其测量精度有限,难以满足深海科学考察、资源勘探开发对高精度海底地形地貌的需求。随着各类大深度水下移动载体(如深海拖体、水下机器人、遥控潜器和载人潜水器)的涌现,特别是各类耐高压测绘声纳的商业化,使大深度近海底精细地形地貌探测成为可能。首先分析了多波束测深声纳、侧扫声纳和测深侧扫声纳等3种测绘声纳的基本原理,然后分别介绍了各类测绘声纳的国内外典型商业化产品,并通过典型实例分析了其在大深度近海底精细测绘中的应用情况。 相似文献
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侧扫声纳系统及其在海洋环境监测和保护中的应用 总被引：3，自引：0，他引：3
总结了侧扫声纳系统在海洋环境监测和保护中的应用,介绍了侧扫声纳基本工作原理及其发展情况,并且对其以后的发展进行了简单探讨。 相似文献
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介绍了多波束测深系统和侧扫声纳系统的工作原理,通过实例说明了多波束测深系统和侧扫声纳系统在海底目标探测的工作流程,总结出两种探测系统在探测海底目标上的优缺点,说明了多种探测手段的综合应用是海底目标探测技术的发展方向。 相似文献
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基于数学形态学的侧扫声呐图像轮廓自动提取 总被引：1，自引：0，他引：1
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。 相似文献
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Automatic Registration of TOBI Side-Scan Sonar and Multi-Beam Bathymetry Images for Improved Data Fusion 总被引：1，自引：0，他引：1
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. 相似文献
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Duncan Tamsett 《Marine Geophysical Researches》1993,15(1):43-64
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. 相似文献
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Dura E. Yan Zhang Xuejun Liao Dobeck G.J. Carin L. 《Oceanic Engineering, IEEE Journal of》2005,30(2):360-371
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. 相似文献