Machine learning for underwater laser detection and differentiation of macroalgae and coral

M. Huot, F. Dalgleish, D. Beauchesne, M. Piché, P. Archambault
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Abstract

A better understanding of how spatial distribution patterns in important primary producers and ecosystem service providers such as macroalgae and coral are affected by climate-change and human activity-related events can guide us in anticipating future community and ecosystem response. In-person underwater field surveys are essential in capturing fine and/or subtle details but are rarely simple to orchestrate over large spatial scale (e.g., hundreds of km). In this work, we develop an automated spectral classifier for detection and classification of various macroalgae and coral species through a spectral response dataset acquired in a controlled setting and via an underwater multispectral laser serial imager. Transferable to underwater lidar detection and imaging methods, laser line scanning is known to perform in various types of water in which normal photography and/or video methods may be affected by water optical properties. Using off the shelf components, we show how reflectance and fluorescence responses can be useful in differentiating algal color groups and certain coral genera. Results indicate that while macroalgae show many different genera and species for which differentiation by their spectral response alone would be difficult, it can be reduced to a three color-type/class spectral response problem. Our results suggest that the three algal color groups may be differentiated by their fluorescence response at 580 nm and 685 nm using common 450 nm, 490 nm and 520 nm laser sources, and potentially a subset of these spectral bands would show similar accuracy. There are however classification errors between green and brown types, as they both depend on Chl-a fluorescence response. Comparatively, corals are also very diverse in genera and species, and reveal possible differentiable spectral responses between genera, form (i.e., soft vs. hard), partly related to their emission in the 685 nm range and other shorter wavelengths. Moreover, overlapping substrates and irregular edges are shown to contribute to classification error. As macroalgae are represented worldwide and share similar photopigment assemblages within respective color classes, inter color-class differentiability would apply irrespective of their provenance. The same principle applies to corals, where excitation-emission characteristics should be unchanged from experimental response when investigated in-situ.
大型藻类和珊瑚水下激光探测与分化的机器学习
更好地了解大型藻类和珊瑚等重要初级生产者和生态系统服务提供者的空间分布格局如何受到气候变化和人类活动相关事件的影响,可以指导我们预测未来的群落和生态系统响应。亲自水下实地调查对于捕捉精细和/或微妙的细节至关重要,但在大空间尺度(例如数百公里)上进行协调很少是简单的。在这项工作中,我们开发了一个自动光谱分类器,通过在受控环境中获得的光谱响应数据集和水下多光谱激光串行成像仪,用于检测和分类各种大型藻类和珊瑚物种。激光线扫描可转移到水下激光雷达探测和成像方法,已知可在各种类型的水中执行,其中正常的摄影和/或视频方法可能受到水的光学特性的影响。使用现成的组件,我们展示了反射和荧光反应如何在区分藻类颜色组和某些珊瑚属中有用。结果表明,虽然大藻属、种众多,仅凭光谱响应难以区分,但可以归结为三色型/类光谱响应问题。我们的研究结果表明,使用常见的450 nm、490 nm和520 nm激光源,可以通过它们在580 nm和685 nm的荧光响应来区分这三种藻类的颜色组,并且这些光谱波段的子集可能会显示出相似的准确性。然而,在绿色和棕色类型之间存在分类错误,因为它们都依赖于Chl-a荧光响应。相比之下,珊瑚在属和种类上也非常多样化,并且在属和形式之间显示可能可区分的光谱响应(即软与硬),部分与它们在685 nm范围内和其他较短波长内的发射有关。此外,重叠的基材和不规则的边缘被证明有助于分类误差。由于大型藻类在世界各地都有分布,并且在各自的颜色类别中具有相似的光色素组合,因此无论其来源如何,颜色类别间的可区分性都适用。同样的原则也适用于珊瑚,在实地调查时,珊瑚的激发-发射特性应与实验响应保持不变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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