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 Garnett, Maria Gerasimchuk-Djordjevic, Mikhail Y Berezin
{"title":"IDCube Lite:用于多光谱和高光谱应用的免费交互式发现立方体软件。","authors":"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 Garnett, Maria Gerasimchuk-Djordjevic, Mikhail Y Berezin","doi":"10.1255/jsi.2021.a1","DOIUrl":null,"url":null,"abstract":"<p><p>Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical, machine vision and other fields. The rapidly increasing number of applications requires convenient easy-to-navigate software that can be used by new and experienced users to analyse data, and develop, apply and deploy novel algorithms. Herein, we present our platform, IDCube Lite, an Interactive Discovery Cube that performs essential operations in hyperspectral data analysis to realise the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimise parameters and obtain visual input for the user in a way not previously accessible with other software packages. 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 that allow users to integrate novel computational methods.</p>","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409277/pdf/","citationCount":"1","resultStr":"{\"title\":\"IDCube Lite: Free Interactive Discovery Cube software for multi- and hyperspectral applications.\",\"authors\":\"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 Garnett, Maria Gerasimchuk-Djordjevic, Mikhail Y Berezin\",\"doi\":\"10.1255/jsi.2021.a1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical, machine vision and other fields. The rapidly increasing number of applications requires convenient easy-to-navigate software that can be used by new and experienced users to analyse data, and develop, apply and deploy novel algorithms. Herein, we present our platform, IDCube Lite, an Interactive Discovery Cube that performs essential operations in hyperspectral data analysis to realise the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimise parameters and obtain visual input for the user in a way not previously accessible with other software packages. 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 that allow users to integrate novel computational methods.</p>\",\"PeriodicalId\":37385,\"journal\":{\"name\":\"Journal of Spectral Imaging\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409277/pdf/\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Spectral Imaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1255/jsi.2021.a1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2021/5/5 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"Chemistry\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Spectral Imaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1255/jsi.2021.a1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/5/5 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"Chemistry","Score":null,"Total":0}
IDCube Lite: Free Interactive Discovery Cube software for multi- and hyperspectral applications.
Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical, machine vision and other fields. The rapidly increasing number of applications requires convenient easy-to-navigate software that can be used by new and experienced users to analyse data, and develop, apply and deploy novel algorithms. Herein, we present our platform, IDCube Lite, an Interactive Discovery Cube that performs essential operations in hyperspectral data analysis to realise the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimise parameters and obtain visual input for the user in a way not previously accessible with other software packages. 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 that allow users to integrate novel computational methods.
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.