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dc.contributor.authorRaita-Hakola, A.-M.
dc.contributor.authorPölönen, I.
dc.contributor.editorPaparoditis, N.
dc.contributor.editorMallet, C.
dc.contributor.editorLafarge, F.
dc.contributor.editorYang, M. Y.
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.accessioned2021-09-13T08:31:46Z
dc.date.available2021-09-13T08:31:46Z
dc.date.issued2021
dc.identifier.citationRaita-Hakola, A.-M., & Pölönen, I. (2021). Piecewise anomaly detection using minimal learning machine for hyperspectral images. In N. Paparoditis, C. Mallet, F. Lafarge, M. Y. Yang, 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 Imaging today, foreseeing tomorrow, Commission III</i> (V-3-2021, pp. 89-96). Copernicus Publications. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. <a href="https://doi.org/10.5194/isprs-annals-V-3-2021-89-2021" target="_blank">https://doi.org/10.5194/isprs-annals-V-3-2021-89-2021</a>
dc.identifier.otherCONVID_100930894
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/77765
dc.description.abstractHyperspectral imaging, with its applications, offers promising tools for remote sensing and Earth observation. Recent development has increased the quality of the sensors. At the same time, the prices of the sensors are lowering. Anomaly detection is one of the popular remote sensing applications, which benefits from real-time solutions. A real-time solution has its limitations, for example, due to a large amount of hyperspectral data, platform’s (drones or a cube satellite) constraints on payload and processing capability. Other examples are the limitations of available energy and the complexity of the machine learning models. When anomalies are detected in real-time from the hyperspectral images, one crucial factor is to utilise a computationally efficient method. The Minimal Learning Machine is a distance-based classification algorithm, which can be modified for anomaly detection. Earlier studies confirms that the Minimal learning Machine (MLM) is capable of detecting efficiently global anomalies from the hyperspectral images with a false alarm rate of zero. In this study, we will show that by using a carefully selected lower threshold besides the higher threshold of the variance, it is possible to detect local and global anomalies with the MLM. The downside is that the improved method is highly sensitive with the respect to the noise. Thus, the second aim of this study is to improve the MLM’s robustness with respect to noise by introducing a novel approach, the piecewise MLM. With the new approach, the piecewise MLM can detect global and local anomalies, and the method is significantly more robust with respect to noise than the MLM. As a result, we have an interesting, easy to implement and computationally light method which is suitable for remote sensing applications.en
dc.format.extent324
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherCopernicus Publications
dc.relation.ispartofXXIV ISPRS Congress Imaging today, foreseeing tomorrow, Commission III
dc.relation.ispartofseriesISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
dc.rightsCC BY 4.0
dc.subject.otherhyperspectral imaging
dc.subject.otherMinimal Learning Machine
dc.subject.otherpiecewise approach
dc.subject.otheranomaly detection
dc.subject.otherreal-time computation
dc.subject.othermachine learning
dc.titlePiecewise anomaly detection using minimal learning machine for hyperspectral images
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202109134858
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.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange89-96
dc.relation.issn2194-9042
dc.relation.volumeV-3-2021
dc.type.versionpublishedVersion
dc.rights.copyright© Author(s) 2021
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Society for Photogrammetry and Remote Sensing Congress
dc.relation.grantnumber327862
dc.subject.ysokoneoppiminen
dc.subject.ysohyperspektrikuvantaminen
dc.subject.ysospektrikuvaus
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p39290
jyx.subject.urihttp://www.yso.fi/onto/yso/p26364
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.5194/isprs-annals-V-3-2021-89-2021
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundinginformationThis study is partly funded by the Academy of Finland (Grant No. 327862).
dc.type.okmA4


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