I-Corps: Interactive Software for Hyperspectral Image Analysis

这个i-Corps项目的更广泛的影响/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能力。高光谱成像，或化学成像，是光谱和数字成像的结合。拟议的系统结合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，通过使光学分选器能够识别和去除缺陷产品来提高产品质量，以及从钻芯和遥感中识别矿物。该软件平台的潜在早期应用将在农业中，以识别作物的早期胁迫，评估土壤健康，量化水资源，并预测收成。这个i-Corps项目进一步开发了一个软件平台，用于深度挖掘来自高光谱成像的数据。该系统在光谱成像领域执行几项基本操作，并为用户提供各种技术熟练程度的高级图像分析。拟议软件的优势在于其直观的设计，使用户能够通过可视的交互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，便于搜索大型数据集中的隐藏信息，在数据分析方面提供了广泛的改进。该程序包无需事先的编程技能即可操作，并允许实时处理当前使用的大多数数据格式。互动性、目标识别和机器学习算法在高光谱图像分析过程中实现了高效率。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.