{"data":{"id":"10.4121/uuid:fad486b6-0dfb-4d23-8025-b24407a08698","type":"dois","attributes":{"doi":"10.4121/uuid:fad486b6-0dfb-4d23-8025-b24407a08698","prefix":"10.4121","suffix":"uuid:fad486b6-0dfb-4d23-8025-b24407a08698","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Melchert, Friedrich","nameType":"Personal","givenName":"Friedrich","familyName":"Melchert","nameIdentifiers":[{"nameIdentifier":"0000-0002-9145-2277","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Matros, A. (Andrea)","givenName":"A. (Andrea)","familyName":"Matros","affiliation":[],"nameIdentifiers":[]},{"name":"Biehl, M. (Michael)","givenName":"M. (Michael)","familyName":"Biehl","affiliation":[],"nameIdentifiers":[]},{"name":"Seiffert, U. (Udo)","givenName":"U. (Udo)","familyName":"Seiffert","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"The sugar dataset - A multimodal hyperspectral dataset for classification and research"}],"publisher":"University of Groningen","container":{},"publicationYear":2016,"subjects":[{"subject":"Information Systems","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0806"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Artificial Intelligence and Image Processing","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0801"},{"subject":"Classification"},{"subject":"Dimensionality Reduction"},{"subject":"Hyperspectral data"},{"subject":"Model Transfer"},{"subject":"Model invariant classification"}],"contributors":[{"name":"Fraunhofer Institute For Factory Operation And Automation IFF, Magdeburg, Germany","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]},{"name":"Leibniz Institute Of Plant Genetics And Crop Plant Research (IPK), Gatersleben, Germany","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]},{"name":"University Of Groningen, Johann Bernoulli Institute For Mathematics And Computer Science, Groningen, The Netherlands","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]}],"dates":[{"date":"2016-06-30","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"References","relatedIdentifier":"http://hdl.handle.net/11370/6dfc58c5-ea01-4fb6-93aa-246142007545","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":[],"formats":["media types: application/pdf, application/zip, text/csv, text/plain"],"version":"1","rightsList":[{"rights":"4TU General Terms of Use","rightsUri":"https://doi.org/10.4121/resource:terms_of_use"}],"descriptions":[{"description":"The sugar dataset is a multimodal hyperspectral dataset of sugar and sugar related substances. The substances that were used for the creation of dataset are: - Sugar Ester S170 - Sugar Ester S770 - Sugar Ester S1570 - Sugar Ester P1570 - D-Mannitol - D-Sorbitol - D-Glucose - D-Galactose - D-Fructose All of the substances were hyperspectrally recorded using different sensors, namely: - Canon EOS 70D - ASD FiledSpec 3 - Neo VNIR-1600 - Neo VNIR-1800 - Neo SWIR-320m-e - Neo SWIR-384 - Nuance Ex The different sensors cover different wavelength ranges as well as different wavelength resolutions. This creates a unique dataset, that not only takles the question of hyperspectral classification, but also enable the research on topics like high dimensional data exploration, sensor invariant classification and dimensionality reduction.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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