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owards Characterizing and Developing Formation and Migration Cues in Seafloor Sand Waves on Topology, Morphology, Evolution from High-Resolution Mapping via Side-Scan Sonar in Autonomous Underwater Vehicles
Sensors ( IF 3.9 ) Pub Date : 2021-05-10 , DOI: 10.3390/s21093283
Rui Nian , Lina Zang , Xue Geng , Fei Yu , Shidong Ren , Bo He , Xishuang Li

Sand waves constitute ubiquitous geomorphology distribution in the ocean. In this paper, we quantitatively investigate the sand wave variation of topology, morphology, and evolution from the high-resolution mapping of a side scan sonar (SSS) in an Autonomous Underwater Vehicle (AUV), in favor of online sequential Extreme Learning Machine (OS-ELM). We utilize echo intensity directly derived from SSS to help accelerate detection and localization, denote a collection of Gaussian-type morphological templates, with one integrated matching criterion for similarity assessment, discuss the envelope demodulation, zero-crossing rate (ZCR), cross-correlation statistically, and estimate the specific morphological parameters. It is demonstrated that the sand wave detection rate could reach up to 95.61% averagely, comparable to deep learning such as MobileNet, but at a much higher speed, with the average test time of 0.0018 s, which is particularly superior for sand waves at smaller scales. The calculation of morphological parameters primarily infer a wave length range and composition ratio in all types of sand waves, implying the possible dominant direction of hydrodynamics. The proposed scheme permits to delicately and adaptively explore the submarine geomorphology of sand waves with online computation strategies and symmetrically integrate evidence of its spatio-temporal responses during formation and migration.

中文翻译:

owards在自主水下航行器中通过侧面扫描声纳从地形,形态,高分辨率成像演变等方面表征和发展海底砂波的形成和迁移线索

沙波构成了海洋中普遍存在的地貌分布。在本文中,我们从自动水下航行器(AUV)的侧面扫描声纳(SSS)的高分辨率映射图中定量研究了沙波的拓扑,形态和演化变化,支持在线顺序极限学习机( OS-ELM)。我们利用直接来自SSS的回波强度来帮助加快检测和定位,表示高斯型形态模板的集合,并使用一个集成的匹配标准进行相似性评估,讨论包络解调,零交叉率(ZCR),互相关统计,并估计特定的形态参数。结果表明,沙波检测率平均可达到95.61%,与MobileNet,但速度要快得多,平均测试时间为0.0018 s,这对于较小规模的沙波尤为优越。形态参数的计算主要是推断所有类型的沙波的波长范围和组成比,这暗示着流体动力学的可能主导方向。提出的方案允许通过在线计算策略精细地和自适应地探索沙波的海底地貌,并对称地整合其形成和迁移过程中时空响应的证据。暗示流体力学的可能主导方向。提出的方案允许通过在线计算策略精细地和自适应地探索沙波的海底地貌,并对称地整合其形成和迁移过程中时空响应的证据。暗示流体力学的可能主导方向。提出的方案允许通过在线计算策略精细地和自适应地探索沙波的海底地貌,并对称地整合其形成和迁移过程中时空响应的证据。
更新日期:2021-05-10
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