{"title":"使用文件碎片的高效高光谱数据处理","authors":"C. Caruncho, P. J. Pardo, H. Cwierz","doi":"10.2352/j.imagingsci.technol.2023.67.5.050403","DOIUrl":null,"url":null,"abstract":"In this article, we present a method for processing hyperspectral data in an easy and quick manner. We explain how we split the hyperspectral cube in different sections to be processed using fewer resources. We describe the processing, which includes extraction of the raw data along with white and black calibration data, calibration of the data and application of desired light source, color space, and gamma transformation. We then present a built-in software, including an easy interactive Graphical User Interface (GUI) that will allow fellow researchers to process hyperspectral images in a simple fashion.","PeriodicalId":15924,"journal":{"name":"Journal of Imaging Science and Technology","volume":"43 1","pages":"0"},"PeriodicalIF":0.6000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient Hyperspectral Data Processing using File Fragmentation\",\"authors\":\"C. Caruncho, P. J. Pardo, H. Cwierz\",\"doi\":\"10.2352/j.imagingsci.technol.2023.67.5.050403\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this article, we present a method for processing hyperspectral data in an easy and quick manner. We explain how we split the hyperspectral cube in different sections to be processed using fewer resources. We describe the processing, which includes extraction of the raw data along with white and black calibration data, calibration of the data and application of desired light source, color space, and gamma transformation. We then present a built-in software, including an easy interactive Graphical User Interface (GUI) that will allow fellow researchers to process hyperspectral images in a simple fashion.\",\"PeriodicalId\":15924,\"journal\":{\"name\":\"Journal of Imaging Science and Technology\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Imaging Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2352/j.imagingsci.technol.2023.67.5.050403\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Imaging Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2352/j.imagingsci.technol.2023.67.5.050403","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY","Score":null,"Total":0}
Efficient Hyperspectral Data Processing using File Fragmentation
In this article, we present a method for processing hyperspectral data in an easy and quick manner. We explain how we split the hyperspectral cube in different sections to be processed using fewer resources. We describe the processing, which includes extraction of the raw data along with white and black calibration data, calibration of the data and application of desired light source, color space, and gamma transformation. We then present a built-in software, including an easy interactive Graphical User Interface (GUI) that will allow fellow researchers to process hyperspectral images in a simple fashion.
期刊介绍：
Typical issues include research papers and/or comprehensive reviews from a variety of topical areas. In the spirit of fostering constructive scientific dialog, the Journal accepts Letters to the Editor commenting on previously published articles. Periodically the Journal features a Special Section containing a group of related— usually invited—papers introduced by a Guest Editor. Imaging research topics that have coverage in JIST include:
Digital fabrication and biofabrication;
Digital printing technologies;
3D imaging: capture, display, and print;
Augmented and virtual reality systems;
Mobile imaging;
Computational and digital photography;
Machine vision and learning;
Data visualization and analysis;
Image and video quality evaluation;
Color image science;
Image archiving, permanence, and security;
Imaging applications including astronomy, medicine, sports, and autonomous vehicles.
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