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[{"key": "dc.contributor.advisor", "value": "P\u00f6l\u00f6nen, Ilkka", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.advisor", "value": "H\u00e4m\u00e4l\u00e4inen, Timo", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Penttil\u00e4, Jeremias", "language": null, "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2017-11-14T11:09:40Z", "language": "", "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2017-11-14T11:09:40Z", "language": "", "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2017", "language": null, "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.other", "value": "oai:jykdok.linneanet.fi:1738562", "language": null, "element": "identifier", "qualifier": "other", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/55868", "language": "", "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Menetelm\u00e4 poikkeavuuksien havaitsemiseen hyperspektrikuvista k\u00e4ytt\u00e4en syvi\u00e4\r\nkonvolutiivisia autoenkoodereita. \r\n\r\nPoikkeavuuksien havaitseminen kuvista, erityisesti hyperspektraalisista kuvista, on hankalaa. Kun ongelmaan yhdistet\u00e4\u00e4n ennalta tuntematon data ja poikkeavuudet, muodostuu ongelma viel\u00e4 laajemmaksi. Spektraalisten poikkeavuuksien havaitsemiseen on kehitetty useita eri menetelmi\u00e4, mutta spatiaalisten poikkeavuuksien havaitseminen on huomattavasti hankalempaa. T\u00e4ss\u00e4 ty\u00f6ss\u00e4 esitell\u00e4\u00e4n uudenkaltainen menetelm\u00e4 sek\u00e4 spatiaalisten ett\u00e4 spektraalisten poikkeavuuksien samanaikaiseen havaitsemiseen. Menetelm\u00e4 on suunniteltu erityisesti spektraaliselle datalle, mutta soveltuu my\u00f6s perinteisille kuville. Menetelm\u00e4ss\u00e4 kolmiulotteisilla konvolutionaalisilla autoenkoodereilla l\u00f6ydet\u00e4\u00e4n koulutus-datassa esiintyvi\u00e4 normaaleja piirteit\u00e4. T\u00e4t\u00e4 verkkoa k\u00e4ytt\u00e4m\u00e4ll\u00e4 voidaan testidata projisoida piirre-avaruuteen. T\u00e4st\u00e4 projisoidusta datasta voidaan etsi\u00e4 poikkeavuuksia k\u00e4ytt\u00e4en perinteisi\u00e4 algoritmeja. Ty\u00f6ss\u00e4 esitet\u00e4\u00e4n kahdet erilliset tulokset. Ensimm\u00e4isiss\u00e4 on esitetty menetelm\u00e4n toimivuus todellisuutta vastaavassa tilanteessa, jossa tietoa poikkeavuuksista ei ole etuk\u00e4teen. N\u00e4iden tulosten lis\u00e4ksi toinen ajo datalla, johon on lis\u00e4tty synteettisi\u00e4 tunnettuja poikkeavuuksia suoritetaan. T\u00e4m\u00e4n toisen ajon tulokset voidaan validoida, koska anomaliat ovat nyt tunnettuja.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Detecting anomalies from any image data, especially hyperspectral ones, is not a trivial task. When combined with the lack of apriori labels or detection targets, it grows even more complex. Detecting spectral anomalies can be done with numerous methods, but the detection of spatial ones is vastly more complicated affair. In this thesis a new way to detect both spatial and spectral anomalies at the same time is proposed. The method has been designed with hyperspectral data in mind, but should work for conventional images also. This is achieved works by using 3-d convolutional autoencoders to learn commonly occurring features both spatial and spectral, across the the test data. By running the test data through this network, the data is transformed to a feature-space. In this space, the images can be analyzed for the presence of anomalies by the means of standard anomaly detection algorithms. A simple real-world use case with unmodified images is presented. Second run for validation purposes is done with data containing synthetic anomalies.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted using Plone Publishing form by Jeremias Penttil\u00e4 (jefepent) on 2017-11-14 11:09:38.386140. Form: Pro gradu -lomake (https://kirjasto.jyu.fi/julkaisut/julkaisulomakkeet/pro-gradu-lomake). JyX data: [jyx_publishing-allowed (fi) =True]", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by jyx lomake-julkaisija (jyx-julkaisija.group@korppi.jyu.fi) on 2017-11-14T11:09:40Z\r\nNo. of bitstreams: 2\r\nURN:NBN:fi:jyu-201711144248.pdf: 9839130 bytes, checksum: 382c5fe91057460eed564c42908f9a11 (MD5)\r\nlicense.html: 4860 bytes, checksum: 6f72df65257a9cbaf939c0ba87a4373d (MD5)", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2017-11-14T11:09:40Z (GMT). No. of bitstreams: 2\r\nURN:NBN:fi:jyu-201711144248.pdf: 9839130 bytes, checksum: 382c5fe91057460eed564c42908f9a11 (MD5)\r\nlicense.html: 4860 bytes, checksum: 6f72df65257a9cbaf939c0ba87a4373d (MD5)\r\n Previous issue date: 2017", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "1 verkkoaineisto (75 sivua)", "language": null, "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "schema": "dc"}, {"key": "dc.language.iso", "value": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "hyperspektrikuvat", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "konvoluutio", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "autoenkooderit", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "machine learning", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "anomaly detection", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "hyperspectral images", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "hdbscan", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "convolutional neural network", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "autoencoder", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "convolutional autoencoder", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "CAE", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "SCAE", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "deep learning", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "autoenkooderi", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "A method for anomaly detection in hyperspectral images, using deep convolutional autoencoders", "language": null, "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, 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"dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietotekniikka", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Mathematical Information Technology", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.date.updated", "value": "2017-11-14T11:09:40Z", "language": "", "element": "date", "qualifier": "updated", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": 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