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水下高光谱成像作为深海大型动物原位分类工具。

Underwater hyperspectral imaging as an in situ taxonomic tool for deep-sea megafauna.

机构信息

Department of Marine Technology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany.

出版信息

Sci Rep. 2018 Aug 27;8(1):12860. doi: 10.1038/s41598-018-31261-4.

Abstract

Identification of benthic megafauna is commonly based on analysis of physical samples or imagery acquired by cameras mounted on underwater platforms. Physical collection of samples is difficult, particularly from the deep sea, and identification of taxonomic morphotypes from imagery depends on resolution and investigator experience. Here, we show how an Underwater Hyperspectral Imager (UHI) can be used as an alternative in situ taxonomic tool for benthic megafauna. A UHI provides a much higher spectral resolution than standard RGB imagery, allowing marine organisms to be identified based on specific optical fingerprints. A set of reference spectra from identified organisms is established and supervised classification performed to identify benthic megafauna semi-autonomously. The UHI data provide an increased detection rate for small megafauna difficult to resolve in standard RGB imagery. In addition, seafloor anomalies with distinct spectral signatures are also detectable. In the region investigated, sediment anomalies (spectral reflectance minimum at ~675 nm) unclear in RGB imagery were indicative of chlorophyll a on the seafloor. Underwater hyperspectral imaging therefore has a great potential in seafloor habitat mapping and monitoring, with areas of application ranging from shallow coastal areas to the deep sea.

摘要

底栖大型动物的识别通常基于安装在水下平台上的摄像机获取的物理样本或图像进行分析。采集样本非常困难,特别是在深海中,而从图像中识别分类形态型则取决于分辨率和调查员的经验。在这里,我们展示了水下高光谱成像仪(UHI)如何可用作底栖大型动物的替代原位分类工具。UHI 提供的光谱分辨率远高于标准 RGB 图像,允许根据特定的光学指纹识别海洋生物。建立了一组来自已识别生物的参考光谱,并执行监督分类以半自动识别底栖大型动物。UHI 数据提高了在标准 RGB 图像中难以分辨的小型大型动物的检测率。此外,还可以检测到具有独特光谱特征的海底异常。在所研究的区域中,RGB 图像中不清晰的沉积物异常(约 675nm 的光谱反射率最小值)表明海底有叶绿素 a。因此,水下高光谱成像在海底栖息地制图和监测方面具有很大的潜力,应用领域从浅海区域到深海。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7788/6110793/6b7eb45eb5ee/41598_2018_31261_Fig1_HTML.jpg

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