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dc.contributor.authorPölönen, Ilkka
dc.contributor.authorRiihiaho, Kimmo
dc.contributor.authorHakola, Anna-Maria
dc.contributor.authorAnnala, Leevi
dc.contributor.editorPaparoditis, N.
dc.contributor.editorMallet, C.
dc.contributor.editorLafarge, F.
dc.contributor.editorJiang, J.
dc.contributor.editorShaker, A.
dc.contributor.editorZhang, H.
dc.contributor.editorLiang, X.
dc.contributor.editorOsmanoglu, B.
dc.contributor.editorSoergel, U.
dc.contributor.editorHonkavaara, E.
dc.contributor.editorScaioni, M.
dc.contributor.editorZhang, J.
dc.contributor.editorPeled, A.
dc.contributor.editorWu, L.
dc.contributor.editorLi, R.
dc.contributor.editorYoshimura, M.
dc.contributor.editorDi, K.
dc.contributor.editorAltan, O.
dc.contributor.editorAbdulmuttalib, H. M.
dc.contributor.editorFaruque, F. S.
dc.date.accessioned2020-09-01T07:47:27Z
dc.date.available2020-09-01T07:47:27Z
dc.date.issued2020
dc.identifier.citationPölönen, I., Riihiaho, K., Hakola, A.-M., & Annala, L. (2020). Minimal learning machine in anomaly detection from hyperspectral images. In N. Paparoditis, C. Mallet, F. Lafarge, J. Jiang, A. Shaker, H. Zhang, X. Liang, B. Osmanoglu, U. Soergel, E. Honkavaara, M. Scaioni, J. Zhang, A. Peled, L. Wu, R. Li, M. Yoshimura, K. Di, O. Altan, H. M. Abdulmuttalib, & F. S. Faruque (Eds.), <i>XXIV ISPRS Congress, Commission III</i> (pp. 467-472). International Society for Photogrammetry and Remote Sensing. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2020. <a href="https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-467-2020" target="_blank">https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-467-2020</a>
dc.identifier.otherCONVID_41834791
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/71570
dc.description.abstractAnomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine for hyperspectral anomaly detection. Minimal learning machine is a novel distance-based classification algorithm, which is now modified to detect anomalies. Besides being computationally efficient, minimal learning machine is also easy to implement. Based on the results, we show that minimal learning machine is efficient in detecting global anomalies from the hyperspectral data with low false alarm rate.en
dc.format.extent1722
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherInternational Society for Photogrammetry and Remote Sensing
dc.relation.ispartofXXIV ISPRS Congress, Commission III
dc.relation.ispartofseriesInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
dc.rightsCC BY 4.0
dc.subject.otherminimal learning machine
dc.subject.otherhyperspectral imaging
dc.subject.otheranomaly detection
dc.subject.otherremote sensing
dc.titleMinimal learning machine in anomaly detection from hyperspectral images
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202009015699
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.description.reviewstatuspeerReviewed
dc.format.pagerange467-472
dc.relation.issn1682-1750
dc.type.versionpublishedVersion
dc.rights.copyright© Authors 2020.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceISPRS Congress
dc.relation.grantnumber327862
dc.subject.ysokaukokartoitus
dc.subject.ysokoneoppiminen
dc.subject.ysospektrikuvaus
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p2521
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p26364
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.5194/isprs-archives-XLIII-B3-2020-467-2020
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundinginformationThis study is partly funded by Academy of Finland (Grant 327862).


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