Idcube Lite – A Free Interactive Discovery Cube Software for Multi And Hyperspectral Applications

Deependra Mishra, Helena Hurbon, John Wang, Steven T. Wang, Tommy Du, Qian Wu, David Kim, Shiva Basir, Qian Cao, Hairong Zhang, Kathleen Xu, Andy Yu, Yifan Zhang, Yunshen Huang, Roman Garrett, Maria Gerasimchuk-Djordjevic, Mikhail Y. Berezin
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Abstract

Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical and machine vision fields. The rapidly increasing number of applications requires a convenient easy-to-navigate software that can be used by new and experienced users to analyze data, develop, apply, and deploy novel algorithms. Herein, we present our platform, IDCube that performs essential operations in hyperspectral data analysis to realize the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimize parameters and obtain visual input for the user. The entire software can be operated without any prior programming skills allowing interactive sessions of raw and processed data. IDCube Lite, a free version of the software described in the paper, has many benefits compared to existing packages and offers structural flexibility to discover new hidden features.
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Idcube Lite -一个免费的交互式发现立方体软件,用于多光谱和高光谱应用
多光谱和高光谱成像模式包含了越来越多的光谱技术,在地理空间、生物医学和机器视觉领域有许多应用。快速增长的应用程序数量需要一个方便易用的软件,新用户和有经验的用户可以使用它来分析数据、开发、应用和部署新的算法。在此,我们展示了我们的平台IDCube,它执行高光谱数据分析的基本操作,以充分发挥光谱成像的潜力。该软件的优势在于其交互功能,使用户能够优化参数并为用户获得可视化输入。整个软件可以在没有任何事先编程技能的情况下操作,允许原始和处理数据的交互会话。IDCube Lite是论文中描述的软件的免费版本,与现有软件包相比,它有许多优点,并提供结构灵活性,可以发现新的隐藏功能。
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来源期刊
Journal of Spectral Imaging
Journal of Spectral Imaging Chemistry-Analytical Chemistry
CiteScore
3.90
自引率
0.00%
发文量
11
审稿时长
22 weeks
期刊介绍: JSI—Journal of Spectral Imaging is the first journal to bring together current research from the diverse research areas of spectral, hyperspectral and chemical imaging as well as related areas such as remote sensing, chemometrics, data mining and data handling for spectral image data. We believe all those working in Spectral Imaging can benefit from the knowledge of others even in widely different fields. We welcome original research papers, letters, review articles, tutorial papers, short communications and technical notes.
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