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.

传统摄影通过简单的颜色表示为人类感知产生图像。高光谱成像（HSI）将成像从人类感知的再现扩展到从紫外到红外波长的精确光谱辐射记录。通过HSI实现的增强识别用于生物学，医学，制药，材料，文化遗产保护和空间操作的大量应用。然而，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、陡峭的学习曲线以及缺乏行业标准的限制。 该项目通过开发一个开源的HSI生态系统（HSI-OSE）来释放HSI的潜力，该生态系统由HSI仪器制造商，学术研究人员，软件提供商和普通用户提供。该项目的创新之处是围绕工具和数据中心建立的HSI-OSE可持续组织结构，该工具和数据中心使社区中的注册用户能够进行持续，开放和异步的贡献。该项目的影响正在加速自然资源监测、气候变化评估、人工制品认证、癌症诊断、病原体检测以及药品和种子质量控制等应用领域的进展。HSI-OSE通过促进制造商、研究人员和用户社区之间的协同作用，通过工具和数据共享以及功能请求，推进了高光谱成像领域的知识。通过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）进一步应用该技术。在每个HSI记录会话中生成描绘成像对象的属性、性质或特征的大量数据。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，以实现许多机器ML/AI应用程序之间的数据共享。HSI-OSE的开源软件工具和硬件设计也促进了HSI技术的新采用者的进入。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。