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

大数据：合作研究：IA：利用水下显微镜图像的分类和属性分类器量化浮游生物多样性

• 批准号: 1546351
• 负责人: Jules Jaffe
• 金额: $ 91.61万
• 依托单位: University of California-San Diego Scripps Inst of Oceanography
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546351&HistoricalAwards=false
• 关键词: BIGDATA; Collaborative; Research; IA; Quantifying

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.

浮游生物在全球生态系统中起着至关重要的作用，构成了海洋食品网的基础，将气氛与深海联系起来，并规范了无数的生态和气候上重要的过程。但是，尽管它们的重要性，但评估浮游生物的丰度和分布的技术仍然有限。单个物种的丰富性的变化尤其不足。这包括在许多沿海系统中具有深远的经济，社会和生态系统影响的有害藻华。诸如网和瓶子之类的传统工具可以在抽样过程中破坏脆弱的生物。另一方面，水下显微镜可以观察到不受干扰的生物，并在其自然环境中观察。新的水下显微镜每天都会产生数千个单个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，必须对成像的生物进行识别和分类。但是，这种显微镜产生的大量图像导致了严重的瓶颈：对图像的识别和分类需要很长的时间才能实现。幸运的是，计算机视觉科学的进步在准确执行此类分类任务方面表现出了巨大的希望。该奖项的主要目标是探索和开发用于浮游生物图像分类的计算机视觉方法。一组仪器专家，一名海洋生态学家和一名计算机科学家，包括两名研究生和一名博士生，将制定，实施和测试方法，以促进有效，准确的自动化浮游生物图像分类的目标。该奖项中的进步将使计算机科学的分类算法能够改进，也可以使浮游生物生态学的大量新数据流构成海洋食品网的基础，将气氛与深海联系起来，并规范全球生物地球化学周期。经常通过批量措施或手动列举单个分类群来研究浮游生物。新型的水下显微镜系统（例如Scripps浮游生物摄像机系统（SPC））每天都会生成数以万计的单个浮游生物图像。但是，如果没有图像的准确注释，那么潜在的科学是有限的。该项目将探索多层，深卷积神经网（CNN）作为自动计算机识别方法的使用；这些技术有望分类全球各种研究小组收集的近一万亿个水下显微镜图像。图像的主要来源将是一对已经从Scripps Inst运行2年的显微镜。海洋学码头，产生了2亿兴趣的地区。该项目将使用一种新方法来建立大量的训练集数据库：一个台式成像系统，该系统能够快速生产成千上万的带注释的图像，以显示所有方向和配置中与现场相同的有机体。基于这些自动收集的训练集以及专家的原位图像的手注释，深层（许多层）CNN将嵌入分类学和属性约束，并将用于对成像的生物进行分类。随着成功，这个庞大的，生长的，分类的分类数据集将实现前所未有的，变革性的，特定于分类群特定的探索浮游生物生态系统的动力学，从小时到几十年。