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海底底质的快速探测和精细划分对海洋工程建设 、海洋资源开发等具有重要意义。多波束探测是目前声学底质遥测 的有效手段之一, 通常提取多波束反向散射强度图像和地形数据中的多维特征结合分类器进行底质分类。一方面, 若特征空 间维数过高, 分类效率会显著降低; 另一方面, 个别特征容易放大原始数据处理过程中仍存留的异常现象。针对这一问题, 本文提出了一种结合 Re1iefF 算法和随机森林 (Random Forest, RF) 算法的多波束底质分类方法。提取反向散射强度和地形 共 16 维特征, 利用Re1iefF 算法进行特征筛选, 排除低相关性特征, 降低特征空间维数, 结合采样点数据进行模型训练以构 建多波束底质分类模型。试验结合随机森林算法对未经特征筛选 、经主成分分析 (Principa1 Component Ana1ysis, PCA) 特征 优化后的特征进行分类实验作为对比。本文方法 Kappa 系数达到 85%, 分类总精度高于 90%, 精度具有明显优势, 耗时也 比较短。可见, 本文提出的结合 Re1iefF 和随机森林模型的多波束底质分类方法可以在保证分类精度的同时对多维特征进行 优化, 有效地提高了分类效率, 可对海底底质分类研究提供参考。 相似文献
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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。 相似文献
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介绍几种典型的海底底质分类技术 总被引:2,自引:0,他引:2
本文主要介绍了四家海洋仪器公司的最新海底底质分类技术。它们分别利用多波束的反向散射强度数据、单波束的回声波形结构数据和旁侧声纳数据。采用了多参数统计分析、波形结构分析和影像属性分析等方法,实现了快速、高效、大面积地对海底底质进行间接的分类。尽管它们的技术各不相同,但都可以分为监督分类方式和非监督分类方式。 相似文献
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多波束声纳底质分类可以高效且全覆盖地认识海底底质类型及其分布特征。应用Simrad EM120多波束声纳对东太平洋海隆附近的"宝石山"热液区进行底质分类,一方面可以研究多波束声参数在深水热液区进行底质分类的效果,挖掘多波束声纳在海底底质分类方面的应用潜力;另一方面,通过研究热液区的底质取样、摄像以及多波束声纳资料,建立综合分析热液区底质的模式,获得热液区的底质类型及其分布特征。通过提取与底质属性相关的声纳回波反向散射强度数据,形成"宝石山"热液区的声纳图像,并结合该区域的取样与海底摄像资料,综合分析该热液区的底质类型及其分布特征,结果表明,沉积物在该热液区的喷口西侧广泛分布,在喷口周围主要是基岩与砾石的混杂底质。 相似文献
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基于自适应增强算法(AdaBoost)结合极限学习机(ELM),通过迭代、调整、优化ELM分类器之间的权值,从而构建了具有强鲁棒性、高精度的ELM-AdaBoost强分类器,增强了现有的ELM分类器的稳定性。以珠江口海区侧扫声呐图像为实验数据,对礁石、砂、泥3类典型底质进行分类识别,该方法的平均分类精度超过90%,优于单一ELM分类器的平均分类精度85.95%,也优于LVQ、BP等传统分类器,且在分类所耗时间上也远少于传统分类器。实验结果表明,本文构建的ELM-AdaBoost方法可有效应用于海底声学底质分类,可满足实时底质分类的需求。 相似文献
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济州岛南部海域海底声呐图像分析与声学底质分类 总被引:2,自引:2,他引:0
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。 相似文献
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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. 相似文献
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Processing Multibeam Backscatter Data 总被引:1,自引:0,他引:1
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. 相似文献
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Seafloor acoustic remote sensing with multibeam echo-sounders and bathymetric sidescan sonar systems 总被引:5,自引:0,他引:5
This paper examines the potential for remote classification of seafloor terrains using a combination of quantitative acoustic backscatter measurements and high resolution bathymetry derived from two classes of sonar systems currently used by the marine research community: multibeam echo-sounders and bathymetric sidescans sonar systems. The high-resolution bathymetry is important, not only to determine the topography of the area surveyed, but to provide accurate bottom slope corrections needed to convert the arrival angles of the seafloor echoes received by the sonars into true angles of incidence. An angular dependence of seafloor acoustic backscatter can then be derived for each region surveyed, making it possible to construct maps of acoustic backscattering strength in geographic coordinates over the areas of interest. Such maps, when combined with the high-resolution bathymetric maps normally compiled from the data output by the above sonar systems, could be very effective tools to quantify bottom types on a regional basis, and to develop automatic seafloor classification routines. 相似文献
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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. 相似文献
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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. 相似文献
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Remote estimation of surficial seafloor properties through the application Angular Range Analysis to multibeam sonar data 总被引:2,自引:2,他引:2
The variation of the backscatter strength with the angle of incidence is an intrinsic property of the seafloor, which can
be used in methods for acoustic seafloor characterization. Although multibeam sonars acquire backscatter over a wide range
of incidence angles, the angular information is normally neglected during standard backscatter processing and mosaicking.
An approach called Angular Range Analysis has been developed to preserve the backscatter angular information, and use it for
remote estimation of seafloor properties. Angular Range Analysis starts with the beam-by-beam time-series of acoustic backscatter
provided by the multibeam sonar and then corrects the backscatter for seafloor slope, beam pattern, time varying and angle
varying gains, and area of insonification. Subsequently a series of parameters are calculated from the stacking of consecutive
time series over a spatial scale that approximates half of the swath width. Based on these calculated parameters and the inversion
of an acoustic backscatter model, we estimate the acoustic impedance and the roughness of the insonified area on the seafloor.
In the process of this inversion, the behavior of the model parameters is constrained by established inter-property relationships.
The approach has been tested using a 300 kHz Simrad EM3000 multibeam sonar in Little Bay, NH. Impedance estimates are compared
to in situ measurements of sound speed. The comparison shows a very good correlation, indicating the potential of this approach for
robust seafloor characterization. 相似文献
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Shallow-water imaging multibeam sonars: A new tool for investigating seafloor processes in the coastal zone and on the continental shelf 总被引:8,自引:1,他引:8
John E. Hughes Clarke Larry A. Mayer David E. Wells 《Marine Geophysical Researches》1996,18(6):607-629
Hydrographic quality bathymetry and quantitative acoustic backscatter data are now being acquired in shallow water on a routine basis using high frequency multibeam sonars. The data provided by these systems produce hitherto unobtainable information about geomorphology and seafloor geologic processes in the coastal zone and on the continental shelf.Before one can use the multibeam data for hydrography or quantitative acoustic backscatter studies, however, it is essential to be able to correct for systematic errors in the data. For bathymetric data, artifacts common to deep-water systems (roll, refraction, positioning) need to be corrected. In addition, the potentially far greater effects of tides, heave, vessel lift/squat, antenna motion and internal time delays become of increasing importance in shallower water. Such artifacts now cause greater errors in hydrographic data quality than bottom detection. Many of these artifacts are a result of imperfect motion sensing, however, new methods such as differential GPS hold great potential for resolving such limitations. For backscatter data, while the system response is well characterised, significant post processing is required to remove residual effects of imaging geometry, gain adjustments and water column effects. With the removal of these system artifacts and the establishment of a calibrated test site in intertidal regions (where the seabed may be intimately examined by eye) one can build up a sediment classification scheme for routine regional seafloor identification.When properly processed, high frequency multibeam sonar data can provide a view of seafloor geology and geomorphology at resolutions of as little as a few decimetres. Specific applications include quantitative estimation of sediment transport rates in large-scale sediment waves, volume effects of iceberg scouring, extent and style of seafloor mass-wasting and delineation of structural trends in bedrock. In addition, the imagery potentially provides a means of quantitative classification of seafloor lithology, allowing sedimentologists the ability to examine spatial distributions of seabed sediment type without resorting to subjective estimation or prohibitively expensive bottom-sampling programs. Using Simrad EM100 and EM1000 sonars as an example, this paper illustrates the nature and scale of possible artifacts, the necessary post-processing steps and shows specific applications of these sonars. 相似文献
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The presently studied numerical model, e.g., composite roughness, is successful for the purpose of seafloor classification employing processed multibeam angular backscatter data from manganese-nodule-bearing locations of the Central Indian Ocean Basin. Hybrid artificial neural network (ANN) architecture, comprised of the self-organizing feature map and learning vector quantization (LVQ), has been implemented as an alternative technique for sea-floor roughness classification, giving comparative results with the aforesaid numerical model for processed multibeam angular backscatter data. However, the composite-roughness model approach is protracted due to the inherent need for processed data including system-gain corrections. In order to establish that tedious processing of raw backscatter values is unessential for efficient classification, hybrid ANN architecture has been attempted here due to its nonparametric approach. In this technical communication, successful employment of LVQ algorithm for unprocessed (raw) multibeam backscatter data indicates true real-time classification application. 相似文献
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Obtaining absolute seafloor backscatter measurements from hydrographic multibeam echosounders is yet to be achieved. We propose a low-cost experiment to calibrate the various acquisition modes of a 30-kHz Kongsberg EM 302 multibeam echosounder in a range of water depths. We use a 38-kHz Simrad EK60 calibrated fisheries split-beam echosounder mounted at 45° angle on the vessel’s hull as a reference for the calibration. The processing to extract seafloor backscatter from the EK60 requires bottom detection, ray tracing and motion compensation to obtain acceptable geo-referenced backscatter measurements from this non-hydrographic system. Our experiment was run in Cook Strait, New Zealand, on well-known seafloor patches in shallow, mid, and deep-water depths. Despite acquisition issues due to weather, our results demonstrate the strong potential of such an approach to obtain system’s absolute calibration which is required for quantitative use of backscatter strength data. 相似文献