基于SVM的海底声纳图像底质识别*

对海底底质声纳图像分割,提取单元特征向量进行主成份分析,选取均值、标准差、对比度、相关系数、能量及同质性作为训练特征向量,优化支持向量机(SVM)参数.利用多分类 SVM 对砂、礁石、泥进行训练,有效、快速地分离出了3种底质类型,测试精度高于80%,表明该方法可有效用于海底底质声纳图像的识别和分类.     

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。     

汕头海域海底光缆路由条件及评价

根据在东南亚至日本海底光缆汕头段（300m水深以浅）路由勘察中所获得的多波束、侧扫声纳、浅地层剖面、柱状取样等资料,结合勘察区的自然环境特征,对路由区的自然环境条件、海底工程地质条件进行了详细的分析,并对该段路由条件进行了综合评价。研究表明,此段路由在大陆架区域路由条件较好,适合海底光缆的铺设;在大陆架与大陆坡结合带路南条件相对较差,应做好应对措施。     

侧扫声纳系统在海底障碍物扫测中的应用

针对海道测量中侧扫声纳位置精度低的问题,利用GPS定位数据、单波束水深数据和侧扫数据,研究侧扫声纳扫海测量中海底障碍物信息精化问题。海上试验数据表明：文中提出的多源水声测量信息联合扫测海底障碍物的方法,可改善单一侧扫系统目标探测的精度,对海底目标物扫测具有较好的适用性。     

Coastal landslides and swelling clays at Røsnæs,Denmark

Prior, D. B., 1973: Coastal landslides and swelling clays at Røsnæs, Denmark. Geografisk Tidsskrift, 72: 45–48. København, September 30, 1973.

The landslide morphology of the Røsnæs area has been examined and materials involved in active landsliding analysed.     

基于多波束声纳数据与反射模型的水下地形重建

利用多波束声纳数据重建水下地形,构建高空间分辨率的数字高程模型(DEM)对于在复杂水下区域的物质勘探、目标检测等方面有重要实用意义.然而,多波束声纳系统直接获得的测深数据空间分辨率有限本文基于多波束声纳系统采集的稀疏测深数据(空间位置)和密集回波强度数据(图像性质)来构建水下复杂地形高空间分辨率数字高程模型.利用采集的...     

多波束后向散射数据地形校正的算法研究

利用多波束反向散射数据进行海底底质分类是目前多波束声纳系统应用的一个热点研究方向.然而,由于海底不规则地形的影响,多波束声纳接收的反向散射信号,往往不能够真实表达海底底质的性质.因此,消除地形对多波束反向散射数据的影响是提高海底底质分类精度的一个重要步骤.本文基于多波束系统的特点,在详尽分析了地形对多波束反响散射数据影...     

Detection of deep water benthic macroalgae using image-based classification techniques on multibeam backscatter at Cashes Ledge,Gulf of Maine,USA

Benthic macroalgae form an important part of temperate marine ecosystems, exhibiting a complex three-dimensional character which represents a vital foraging and spawning ground for many juvenile fish species. In this research, image-based techniques for classification of multibeam backscatter are explored for the detection of benthic macroalgae at Cashes Ledge in the Gulf of Maine, USA. Two classifications were performed using QTC-Multiview, differentiated by application of a threshold filter, and macroalgal signatures were independently extracted from the raw sonar datagrams in Matlab. All classifications were validated by comparison with video ground-truth data. The unfiltered classification shows a high degree of complexity in the shallowest areas within the study site; the filtered demonstrates markedly less variation by depth. The unfiltered classification shows a positive agreement with the video ground-truth data; 82.6% of observations recording Laminaria sp., 39.1% of Agarum cribrosum and 100.0% (n = 3) of mixed macroalgae occur within the same acoustically distinct group of classes. These are discrete from the 8.1% recorded agreement with absences and nulls (>40 m) of macrophytes (n = 32) from a total of 86 ground-truth locations. The results of the water column data extraction (WCDE) show similar success, accurately predicting 78.3% of Laminaria sp. and 30.4% of A. cribrosum observations.     

SHADOWS合成孔径声纳系统及性能测试

与传统的侧扫声纳相比,合成孔径声纳系统具有全覆盖无遗漏扫测,测量范围大、速度快,图像分辨率高等优点。本文介绍我国引进第一套SHADOWS合成孔径声纳系统的技术性能,分析SHADOWS合成孔径声纳系统性能测试情况及实际测量中的应用价值,提出了存在的问题及其对策。