基于多维度声学特征优选的多波束海底底质分类

海底表层底质分布信息的准确获取在构建海洋基础地理数据库中发挥着重要作用。目前,多波束是实现大范围海底底质分类的有效手段之一,基于多波束测深和反向散射强度数据所派生的声学特征被广泛应用于底质分类建模。然而,随着特征维度的增加,特征空间中存在的无关和冗余特征严重影响底质分类精度。为了定量评估声学特征对底质类别的表征能力,并消除无效特征对分类结果的干扰,本文提出了基于多维度声学特征优选的海底底质分类方法。首先,结合实际底质样本的物理属性对多维特征进行排序和优选,排除冗余和无关特征。其次,分别应用支持向量机、随机森林和深度信念网络构建海底底质监督分类模型。通过利用爱尔兰海南部多波束调查数据和实地取样信息进行试验,结果表明提出方法对海底底质的总体分类精度和Kappa系数分别最高达到了86.20%和0.834,相较于主成分分析和熵指标特征选择方法有明显提高,突出了该方法在海底底质探测及制图的应用潜力。     

多波束回声数据的统计与底质分类应用

利用声反向散射数据作海底沉积物分类,是海洋地质学家感兴趣的话题,也是目前多波束声纳应用的一个研究热点。结合胶州湾实际调查数据,探讨了贝叶斯分类方法在该领域的应用。研究结果表明,该方法可以对不同的底质类型进行分类,可以识别未知的底质类型以及对混合在一起的两种不同类型的目标进行分类。     

用于海底底质分类的多波束声强数据选取研究

高质量的海底声强图是进行多波束海底底质分类、目标识别的基础。要得到"单纯"反映海底底质信息的声强图,就需要对原始声强数据进行地形改正,消除地形因素的影响。在描述了多波束数据中水深数据不能满足声强数据的改正要求问题的基础上,提出了以水深数据覆盖范围为约束的声强数据选取方法。实例计算结果表明:该方法在能有效地选取高质量的声强数据,提高了基于声强图像的海底底质分类精度。     

K-均值聚类算法在多波束底质分类中的应用

针对海底采样点较少时,监督学习训练分类模型困难的问题,研究无监督学习的K-均值聚类分析算法在多波束海底底质分类中的应用。在探讨K-均值聚类分析算法原理的基础上,构建海底底质分类器,针对分类器需预先输入分类结果种类(K值)这一问题,提出了基于底质采样点和分类效果连续性为原则的K值确定方法。实验结果表明:基于K-均值聚类分析算法的海底底质分类器能较好的实现海底底质类型的自动划分,适用于海量多波束底质特征参数的分类。     

基于改进BP神经网络的海底底质分类

通过采用遗传算法优化神经网络初始权值的方法,将GA算法与BP神经网络有机结合,应用于海底底质分类。基于多波束测深系统获取的反向散射强度数据,应用改进的BP神经网络分类方法,实现对海底基岩、砾石、砂、细砂和泥等底质类型的快速、准确识别。通过实验比较,GA-BP神经网络分类精度明显高于BP神经网络,证明了该方法的有效性和可靠性。     

多波束海底底质分类软件Simrad Triton的应用

研究了利用声学信号对海底底质自动分类的技术。介绍了挪威Simrad公司海底底质分类软件Triton的分类原理，它所应用的分类方法及其软件的体系结构。最后，用实例来说明Simrad Triton在海底底质分类中的应用。     

高分辨率波束形成器在多波束测深系统中的应用

对于海底地形测量,基于FT波束形成的幅度检测法空间分辨率较低,只能较准确给出有限测点的水深信息;平坦海底前提下,分裂子阵检测法或多子阵检测法可以得到连续测点的水深信息,复杂海底地形条件下,这两种方法均难以应用。能否利用高分辨率波束形成器来提高测深系统的空间分辨率是一个值得研究的问题。使用ESPRIT波束形成器处理了多波束测深系统的试验数据,并就其性能与FT波束形成器进行了比较与分析。     

济州岛南部海域海底声呐图像分析与声学底质分类

东海北部外陆架靠近济州岛南部海域,是黄海槽向冲绳海槽延伸的部分,属于黑潮分支黄海暖流的通道入口,分布着脊槽相间的海底底形,对其海底声呐图像的处理分析及声学底质分类的分析研究,有助于了解该通道海底底形表层纹理特征及沉积物分布规律。基于在济州岛南部海域获取的多波束声呐数据,应用图像处理技术和方法,对数据进行了处理,获得了海底声呐影像图,并对其表层纹理特征进行了描述和分析;同时,基于多波束反向散射强度数据,结合19组海底地质取样数据,建立研究区海底反向散射强度与沉积物粒度特征之间的统计关系模型,并以改进的学习向量量化神经网络方法,实现对海底粉砂质砂、黏土质砂以及砂-粉砂-黏土3种底质类型的快速自动分类识别。     

Seafloor Geomorphology and Processes on the Western Continental Margin of India based on Multibeam Acoustic Backscatter Data Analysis

An assessment of the multibeam sonar data of the central Western Continental Margins of India has been carried out to evaluate the seafloor geomorphology and processes by examining the geomorphological attributes e.g., slope, sediments, structures, etc. associated with geomorphic features. The variation in relief and the features located in the region have been mapped and interpreted collectively by utilizing several geospatial mapping tools. The backscatter strength across the area, apparently congruent with the local relief, has helped to examine the sediment movement on the seafloor. The prominent features found in the region include faults, pockmarks, mounds, submarine terraces, and submerged fossil reefs. Several areas with varying topography engender comparable fractal dimension at short scale breaks, and the probability density functions (PDFs) utilizing backscatter data depicting overlapping classes. The present study highlights how fractals and scale break parameters can be utilized to determine the seafloor processes and associated sedimentological dynamics in a complex geographical environment with strong bottom currents, seasonal upwelling, and faulted structure. The role and impact of the various geomorphic processes on the reworking of sediment movement and the overall progression of the seafloor morphology has been revealed for the first time in this part of the ocean bottom.     

基于回波时间的多波束测深与声纳数据匹配方法

以多波束精确的水深数据为参照源,采用原始回波时间对多波束测深数据与其同源声纳数据进行匹配,从而获得高精度和高分辨率的海底影像数据,并避免了传统声纳图像处理过程中斜距改正所带来的几何形变。匹配结果采用光照图输出,并与三维水深图、原始声纳图像和CARIS处理后的声纳图像进行比较分析。该方法有效地提高了多波束数据的利用率,增强了对海底地形的探测分辨率。     

Processing Multibeam Backscatter Data

A new highly precise source of data has recently become available using multibeam sonar systems in hydrography. Multibeam sonar systems can provide hydrographic quality depth data as well as high-resolution seafloor sonar images. We utilize the seafloor backscatter strength data of each beam from multibeam sonar and the automatic classification technology so that we can get the seafloor type identification maps. In this article, analyzing all kinds of error effects in backscatter strength, data are based on the relationship between backscatter strength and seafloor types. We emphasize particularly analyzing the influences of local bottom slope and near nadir reflection in backscatter strength data. We also give the correction algorithms and results of these two influent factors. After processing the raw backscatter strength data and correcting error effects, we can get processed backscatter strength data which reflect the features of seafloor types only. Applying the processed backscatter strength data and mosaicked seafloor sonar images, we engage in seafloor classification and geomorphy interpretation in future research.     

Processing Multibeam Backscatter Data

A new highly precise source of data has recently become available using multibeam sonar systems in hydrography. Multibeam sonar systems can provide hydrographic quality depth data as well as high-resolution seafloor sonar images. We utilize the seafloor backscatter strength data of each beam from multibeam sonar and the automatic classification technology so that we can get the seafloor type identification maps. In this article, analyzing all kinds of error effects in backscatter strength, data are based on the relationship between backscatter strength and seafloor types. We emphasize particularly analyzing the influences of local bottom slope and near nadir reflection in backscatter strength data. We also give the correction algorithms and results of these two influent factors. After processing the raw backscatter strength data and correcting error effects, we can get processed backscatter strength data which reflect the features of seafloor types only. Applying the processed backscatter strength data and mosaicked seafloor sonar images, we engage in seafloor classification and geomorphy interpretation in future research.     

Seafloor Classification of Multibeam Sonar Data Using Neural Network Approach

In this study, the self-organizing map (SOM), which is an unsupervised clustering algorithm, and a supervised proportional learning vector quantization (PLVQ), are employed to develop a combined method of seafloor classification using multibeam sonar backscatter data. The PLVQ is a generalized learning vector quantization based on the proportional learning law (PLL). The proposed method was evaluated in an area where there are four types of sediments. The results show that the performance of the proposed method is better than the SOM and a statistical classification method.     

Seafloor Classification of Multibeam Sonar Data Using Neural Network Approach

In this study, the self-organizing map (SOM), which is an unsupervised clustering algorithm, and a supervised proportional learning vector quantization (PLVQ), are employed to develop a combined method of seafloor classification using multibeam sonar backscatter data. The PLVQ is a generalized learning vector quantization based on the proportional learning law (PLL). The proposed method was evaluated in an area where there are four types of sediments. The results show that the performance of the proposed method is better than the SOM and a statistical classification method.     

多波束回声测深系统测量数据的分离提取方法

通过对Em系列多波束测深系统原始数据结构分析,提出了从原始测量数据中提取各种测量数据的技术方法,并编程实现了该方法。该技术方法对多波束测量数据的分析处理有重要的意义。     

多波束海底地形图的制作方法探讨

提出了采用高斯距离函数加权平均算法,对离散多波束数据进行网格化,该算法的优点是权函数收敛较快,能较好地保留细小的地形特征,而且运算速度较快。对于图形制作方面,提出了2D等深线图叠加3D阴影图模式的海底地形图的绘制思路及实现方法,最终成果图立体感强,可以更好地表现出地形细节。最后就如何改进海底地形图的配色方案进行了探讨。     

多波束回波强度数据记录方式比较

分析了三种不同多波束测深系统回波强度的记录方式及数据结构,基于各自生成声纳图像的特点规律的差异,按其声纳图像不同用途对多波束测深系统进行了归类,其结果可为用户结合自身需求,正确购置多波束测深系统及合理应用声纳图像提供参考.