I-Corps: Interactive Software for Hyperspectral Image Analysis

I-Corps：用于高光谱图像分析的交互式软件

• 批准号: 2138150
• 负责人: Mikhail Berezin
• 金额: $ 5万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138150&HistoricalAwards=false
• 关键词: Corps; Interactive; Software; Hyperspectral; Image

The broader impact/commercial potential of this I-Corps project is to simplify the workflow, reduce analysis time, and enable deep searching capabilities across hyperspectral datasets using a new computational platform. Hyperspectral imaging, or chemical imaging, is the combination of spectroscopy and digital imaging. The proposed system incorporates advanced imaging and machine learning algorithms to streamline data processing and enable users to focus on the application itself, rather than spending time and money on developing image processing algorithms. Hyperspectral imaging is broadly used to diagnose diseases and measure a patient's response to therapy, to enhance product quality by enabling optical sorters to identify and remove defective products, and to identify minerals from drill cores and remote sensing. A potential early application for the software platform will be in agriculture to identify early stress in crops, assess soil health, quantify water resources, and predict harvest yields.This I-Corps project further develops a software platform for deep mining of data derived from hyperspectral imaging. The system performs several essential operations in the spectral imaging domain and enables advanced image analysis for users across a range of technical proficiencies. The strength of the proposed software lies in its intuitive design that enables the user to perform high level data analysis from image processing to machine learning via a visual, interactive interface. Built around a collection of current and proprietary spectral imaging algorithms, the software facilitates the search of hidden information inside large datasets, providing broad improvements in data analysis. The package can be operated without prior programming skills and allows most currently used data formats to be processed in real time. The interactivity, object identification, and machine learning algorithms enable high efficiency during analysis of hyperspectral images.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.

这个I-Corps项目更广泛的影响/商业潜力是简化工作流程，减少分析时间，并使用新的计算平台实现跨高光谱数据集的深度搜索功能。高光谱成像或化学成像是光谱学和数字成像的结合。 该系统集成了先进的成像和机器学习算法，以简化数据处理，使用户能够专注于应用程序本身，而不是花费时间和金钱来开发图像处理算法。高光谱成像被广泛用于诊断疾病和测量患者对治疗的反应，通过使光学分选机能够识别和去除有缺陷的产品来提高产品质量，以及从岩心和遥感中识别矿物。 该软件平台的一个潜在早期应用将是在农业中识别作物的早期压力，评估土壤健康，量化水资源，并预测收成。该系统在光谱成像领域执行几项基本操作，并为一系列技术专业的用户提供高级图像分析。该软件的优势在于其直观的设计，使用户能够通过可视化的交互式界面执行从图像处理到机器学习的高级数据分析。该软件围绕一系列当前和专有的光谱成像算法构建，有助于搜索大型数据集内的隐藏信息，从而为数据分析提供广泛的改进。该软件包可以在没有事先编程技能的情况下操作，并允许真实的时间处理大多数当前使用的数据格式。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。