BIGDATA: Collaborative Research: IA: Quantifying Plankton Diversity with Taxonomy and Attribute Based Classifiers of Underwater Microscope Images

浮游生物在全球生态系统中发挥着至关重要的作用，形成了海洋食物网的基础，将大气与深海联系起来，并调节着无数具有生态和气候意义的重要过程。然而，尽管浮游生物很重要，但评估浮游生物丰度和分布的技术一直有限。个别物种丰度的变化尤其难以解决；这包括对许多沿海系统具有深远的经济、社会和生态影响的有害藻类水华。传统的工具，如网和瓶子，会在采样过程中破坏脆弱的生物体。另一方面，水下显微镜可以在自然环境中不受干扰地观察生物体。新的水下显微镜每天产生数以千计的浮游生物的高分辨率图像。在这些图像可以用于科学分析之前，必须对成像的生物体进行识别和分类。然而，这种显微镜产生的大量图像导致了一个严重的瓶颈：图像的识别和分类对于个人来说需要很长时间才能完成。幸运的是，计算机视觉科学的进步在准确执行这种分类任务方面显示出了巨大的希望。该奖项的主要目标是探索和开发浮游生物图像分类的计算机视觉方法。一个由仪器专家、海洋生态学家和计算机科学家组成的团队，包括两名研究生和一名博士后，将制定、实施和测试方法，以推进高效和准确的浮游生物图像自动分类的目标。这一奖项的进展将使计算机科学中改进的分类算法和浮游生物生态学的大量新数据流成为可能。浮游生物形成海洋食物网的基础，将大气与深海联系起来，并调节全球生物地球化学循环。浮游生物的研究通常是通过整体测量，或通过人工计数个别类群。新的水下显微镜系统，如斯克里普斯浮游生物摄像系统(SPCS)，每天产生数以万计的浮游生物图像。然而，如果没有对图像的准确注释，潜在的科学是有限的。该项目将探索使用多层深度卷积神经网络(CNN)作为自动计算机识别方法；这些技术有望对全球多个研究小组收集的近1万亿张水下显微镜图像进行分类。图像的主要来源将是一对已经从斯克里普斯Inst运行了两年的显微镜。拥有2亿个名胜古迹。该项目将使用一种新的方法建立一个大型训练集数据库：一种台式成像系统，能够快速生成数千张带注释的图像，显示与实地相同的所有方向和形态的生物体。基于这些自动收集的训练集和来自专家的现场图像的手工注释，深层(多层)CNN将嵌入分类和属性约束，并将用于对图像中的生物进行分类。如果成功，这个庞大的、不断增长的、分类分类的数据集将使人们能够在从几个小时到几十年的时间尺度上对浮游生态系统的动态进行前所未有的、变革性的、特定于分类群的探索。

Plankton play an essential role in the global ecosystem, forming the base of marine food webs, linking the atmosphere to the deep ocean, and regulating a myriad of ecologically and climatologically important processes. Despite their importance, however, the technology to assess abundances and distributions of plankton has been limited. Changes in abundances of individual species are particularly poorly resolved; this includes the harmful algal blooms that have profound economic, societal, and ecosystem effects in many coastal systems. Traditional tools such as nets and bottles can destroy fragile organisms during sampling. Underwater microscopes, on the other hand, allow observation of the organisms undisturbed, and in their natural setting. New underwater microscopes are generating many thousands of high-resolution images of individual plankton each day. Before these images can be used for scientific analyses, the imaged organisms must be identified and classified. However, the vast number of images generated by such microscopes has led to a serious bottleneck: identification and classification of the images takes an impossibly long time for individuals to accomplish. Fortunately, advances in computer vision science have shown great promise in accurately performing such classification tasks. The main goal of this award is to explore and develop computer vision approaches for plankton image classification. A team of instrumentation specialists, an ocean ecologist, and a computer scientist, including two graduate students and one post doctoral student, will formulate, implement, and test methods to advance the goal of efficient and accurate automated plankton image classification. The advances made in this award will enable both improved classification algorithms in computer science, and vast new data streams for plankton ecology.Plankton form the base of marine food webs, link the atmosphere to the deep ocean, and regulate global biogeochemical cycles. Plankton are often studied either through bulk measures, or by manual enumeration of individual taxa. Novel underwater microscope systems such as the Scripps Plankton Camera System (SPCS) are generating tens of thousands of images of individual plankton daily. However, without accurate annotation of the images, the potential science is limited. This project will explore the use of many-layer, deep Convolutional Neural Nets (CNN) as automated computer recognition methods; these techniques hold promise for classifying the nearly one trillion underwater microscope images that have been collected by a variety of research groups around the globe. The primary source of images will be a pair of microscopes that have been operating for 2 years from the Scripps Inst. of Oceanography's pier, yielding 200 million regions of interest. The project will build a large data base of training sets using a novel approach: a bench-top imaging system that is capable of rapidly producing thousands of annotated images showing organisms in all orientations and configurations identical to that in the field. Based on these automatically collected training sets, and hand annotation of in situ images from experts, a deep (many layer) CNN will embed taxonomic and attribute constraints, and will be used to classify the organisms imaged. With success, this massive, growing, taxonomically classified dataset will enable unprecedented, transformative, taxon-specific explorations of the dynamics of the planktonic ecosystem on time scales from hours to decades.