采用方向自适应密度聚类自动检测侧扫声呐图像海底线

Automatic Tracking Sea Bottom Line on Side-Scan Sonar Image by Direction Adaptive DBSCAN

  • 摘要: 侧扫声呐是获取海底地貌图像的主要手段之一,海底线是侧扫声呐瀑布图像最显著的特征,准确检测和跟踪海底线是侧扫声呐数据精细处理的基础。受水体环境噪声、船体、水面及水体悬浮目标散射等干扰,传统阈值法及相关图像特征检测算法难以实现海底线自动、准确、高效提取。充分考虑侧扫声呐海底线的边缘特性及沿航迹向密集分布的空间特点,提出了一种边缘方向适应性密度聚类和聚类链筛选相结合的海底线检测方法。该方法通过高斯一阶导卷积模板及非极大值抑制实现高噪声图像边缘梯度和方向计算以及边缘特征的细化;通过设置窄带状搜索邻域,并依据边缘梯度方向实时调整搜索邻域的长轴,以实现对方向变化的线状特征的密度聚类;通过构建基于边缘特征密度聚类的海底线检测策略,包括设定经验范围、阈值法构建聚类种子集、长链原则、排他原则、对称原则、趋势延伸原则、修复原则等,以实现海底线边缘特征的快速密度聚类成链和海底线的筛选。实验验证和对比分析的结果表明,在持续噪声、复杂悬浮物等常见水体回波干扰下,所提方法在海底线检测的准确性和稳定性上优于传统阈值方法,且单呯平均检测耗时仅为0.661 ms。所提侧扫声呐图像海底线检测方法有较好的稳定性和干扰普适性,可在侧扫声呐数据采集和事后处理中推广应用。

     

    Abstract:
    Objectives Side scan sonar is one of the main means to acquire the submarine geomorphology image. Sea bottom line is the most prominent feature of the side scan sonar waterfall image. Accurate detection and tracking of the bottom line is the basis of the fine processing of the side scan sonar data.
    Methods Traditional threshold methods and related image feature detection algorithm are difficult to achieve automatic, accurate, and efficient extraction of the sea bottom line due to the interference of water environment noise and scattering sound from the hull, water surface, and other suspended objects in the water. We fully consider the image edge feature of the sea bottom line and its spatial characteristics that distribute along the track line, thus forming a kind of sea bottom line tracking method with the combining of adaptive edge direction density-based spatial clustering and clustering chain screening. The main work includes three parts: First, image edge gradient and direction are calculated through the Gaussian first-order derivative convolution template, edge features are refined by non-maximum suppression. Second, the density-based spatial clustering algorithm is improved with searching direction adaptively by setting the narrow band-shaped search neighborhood and dynamically adjusting the long axis of the search neighborhood based on the direction of the edge gradient. Third, a serial of sea bottom line tracking strategy based on the clustered edge chains set is constructed, which includes setting experience range, constructing the clustering seed set by threshold method, the long chain principle, the exclusion principle, the symmetry principle, the trend extension principle, the repair principle, etc.
    Results Through experimental verification and comparative analysis, the results show that the accuracy and stability of the proposed method are superior to the traditional threshold method, even in the case of common water echo interference such as continuous noise and complex suspended objects, the average single ping detection time is only 0.661 ms.
    Conclusions The sea-bottom line detection method of side-scan sonar image has good stability and anti-interference performance, and has great potential to be a generalized method for onboard data acquisition and post-processing of side-scan sonar.

     

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