POSE: Phase II: An Open Source Hyperspectral Imaging Ecosystem

POSE：第二阶段：开源高光谱成像生态系统

• 批准号: 2303328
• 负责人: Holly Rushmeier
• 金额: $ 150万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2303328&HistoricalAwards=false
• 关键词: POSE; Phase; II; Open; Source

Traditional photography produces images for human perception with a simple representation of color. Hyperspectral Imaging (HSI) expands imaging beyond reproduction for human perception to precise spectral radiance recording ranging from the ultraviolet to the infrared wavelengths. The enhanced discernment enabled by HSI is used to benefit an abundance of applications across biology, medicine, pharmaceuticals, materials, cultural heritage preservation, and space operations. However, the application of HSI in the past has been inhibited by dispersed intellectual property, siloed and immature software tools and data, steep learning curves, and lack of industry standards. This project is releasing the potential of HSI by developing an open-source HSI ecosystem (HSI-OSE) with contributions from HSI instrument manufactures, academic researchers, software providers, and general users. The project’s novelties are a sustainable organizational structure for the HSI-OSE established around a tool and data hub that enables continuous, open, and asynchronous contributions from registered users in the community. The project's impacts are accelerating progress in application areas such as natural resource monitoring, climate change assessment, artifact authentication, cancer diagnostics, pathogen detection, and quality control in pharmaceuticals and seeds.The HSI-OSE advances knowledge in the field of hyperspectral imaging by promoting synergy among the community of manufacturers, researchers, and users with tools and data sharing, as well as feature requests. With a central hub for HSI data resources, it enables further applications of the technology through machine learning (ML) and artificial intelligence (AI). Large amounts of data are generated in each HSI recording session depicting the attributes, properties, or characteristics of the imaging subject. Sophisticated software tools are required to process and interpret these data. This project creates a comprehensive HSI data resource and develops the protocols and standards for software development to enable data sharing across many machine ML/AI applications. The well-documented open-source software tools and hardware designs from the HSI-OSE also facilitate the entry of new adopters of the HSI technology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

传统摄影以简单的颜色代表制作人类感知的图像。高光谱成像（HSI）扩展了超越人类感知的繁殖成像，从紫外线到红外波长的精确光谱辐射记录。 HSI支持的增强识别用于使生物学，医学，药品，材料，文化遗产准备和空间操作中的大量应用受益。但是，过去，HSI的应用受到分散的知识产权，孤立和不成熟的软件工具和数据，钢制学习曲线以及缺乏行业标准的抑制。该项目通过开发开源HSI生态系统（HSI-SOS）来释放HSI的潜力，并提供HSI仪器制造商，学术研究人员，软件提供商和普通用户的贡献。该项目的新颖性是围绕工具和数据中心建立的HSI-OSE的可持续组织结构，该工具和数据中心可以从社区中注册用户提供持续，开放和异步的贡献。该项目的影响正在加快应用领域的进展，例如自然资源监测，气候变化评估，临床身份验证，癌症诊断，病原体检测以及药品和种子的质量控制。HSI-SOSE在高超光谱领域的知识在高光谱领域的知识在高超光谱领域的知识，并通过促进工具，研究者和用户以及工具和数据的构成社区，以及使用工具和数据的来源，以及数据。借助用于HSI数据资源的中央枢纽，它可以通过机器学习（ML）和人工智能（AI）实现技术的进一步应用。每个HSI记录会话中都会生成大量数据，描述了成像主题的属性，属性或特征。需要复杂的软件工具来处理和解释这些数据。该项目创建了一个全面的HSI数据资源，并开发了软件开发的协议和标准，以启用许多机器ML/AI应用程序的数据共享。 HSI-OSE的有据可查的开源软件工具和硬件设计也赞成进入HSI技术的新采用者。该奖项反映了NSF的法定任务，并认为值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持。