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dc.contributor.authorRaita-Hakola, Anna-Maria
dc.contributor.authorPölönen, Ilkka
dc.contributor.editorJiang, J.
dc.contributor.editorShaker, A.
dc.contributor.editorZhang, H.
dc.date.accessioned2022-08-16T09:19:24Z
dc.date.available2022-08-16T09:19:24Z
dc.date.issued2022
dc.identifier.citationRaita-Hakola, A.-M., & Pölönen, I. (2022). Updating strategies for distance based classification model with recursive least squares. In J. Jiang, A. Shaker, & H. Zhang (Eds.), <i>XXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, Commission III</i> (V-3-2022, pp. 163-170). Copernicus Publications. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. <a href="https://doi.org/10.5194/isprs-annals-V-3-2022-163-2022" target="_blank">https://doi.org/10.5194/isprs-annals-V-3-2022-163-2022</a>
dc.identifier.otherCONVID_150901592
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/82587
dc.description.abstractThe idea is to create a self-learning Minimal Learning Machine (MLM) model that is computationally efficient, easy to implement and performs with high accuracy. The study has two hypotheses. Experiment A examines the possibilities of introducing new classes with Recursive Least Squares (RLS) updates for the pre-trained self learning-MLM model. The idea of experiment B is to simulate the push broom spectral imagers working principles, update and test the model based on a stream of pixel spectrum lines on a continuous scanning process. Experiment B aims to train the model with a significantly small amount of labelled reference points and update it continuously with (RLS) to reach maximum classification accuracy quickly. The results show that the new self-learning MLM method can classify new classes with RLS update but with a cost of decreasing accuracy. With a larger amount of reference points, one class can be introduced with reasonable accuracy. The results of experiment B indicate that self-learning MLM can be trained with a few reference points, and the self-learning model quickly reaches accuracy results comparable with nearest-neighbour NN-MLM. It seems that the self-learning MLM could be a comparable machine learning method for the application of hyperspectral imaging and remote sensing.en
dc.format.extent711
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.otherRecursive Least Squares
dc.subject.otherclassification
dc.subject.otherreal-time computation
dc.subject.othermachine learning
dc.titleUpdating strategies for distance based classification model with recursive least squares
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202208164131
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineLaskennallinen tiedefi
dc.contributor.oppiaineComputing, Information Technology and Mathematicsfi
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineComputational Scienceen
dc.contributor.oppiaineComputing, Information Technology and Mathematicsen
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.pagerange163-170
dc.relation.issn2194-9042
dc.relation.volumeV-3-2022
dc.type.versionpublishedVersion
dc.rights.copyright© Author(s) 2022.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Society for Photogrammetry and Remote Sensing Congress
dc.relation.grantnumber327862
dc.subject.ysohyperspektrikuvantaminen
dc.subject.ysoluokitus (toiminta)
dc.subject.ysokoneoppiminen
dc.subject.ysokaukokartoitus
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p39290
jyx.subject.urihttp://www.yso.fi/onto/yso/p12668
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p2521
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.5194/isprs-annals-V-3-2022-163-2022
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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