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dc.contributor.authorBlix, Katalin
dc.contributor.authorEltoft, Torbjørn
dc.date.accessioned2019-03-19T14:31:29Z
dc.date.available2019-03-19T14:31:29Z
dc.date.issued2018-03-22
dc.description.abstractThis paper evaluates two alternative regression techniques for oceanic chlorophyll-a (Chl-a) content estimation. One of the investigated methodologies is the recently introduced Gaussian process regression (GPR) model. We explore two feature ranking methods derived for the GPR model, namely sensitivity analysis (SA) and automatic relevance determination (ARD). We also investigate a second regression method, the partial least squares regression (PLSR) for oceanic Chl-a content estimation. Feature relevance in the PLSR model can be accessed through the variable importance in projection (VIP) feature ranking algorithm. This paper thus analyzes three feature ranking models, SA, ARD, and VIP, which are all derived from different fundamental principles, and uses the ranked features as inputs to the GPR and PLSR to assess regression strengths. We compare the regression performances using some common performance measures, and show how the feature ranking methods can be used to find the lowest number of features to estimate oceanic Chl-a content by using the GPR and PLSR models, while still producing comparable performance to the state-of-the-art algorithms. We evaluate the models on a global MEdium Resolution Imaging Spectrometer matchup dataset. Our results show that the GPR model has the best regression performance for most of the input feature sets we used, and our conclusion is this model can favorably be used for Chl-a content retrieval, already with two features, ranked by either the SA or ARD methods.en_US
dc.descriptionAccepted manuscript version. Published version available at <a href=https://doi.org/10.1109/JSTARS.2018.2810704>https://doi.org/10.1109/JSTARS.2018.2810704. </a>en_US
dc.identifier.citationBlix, K. & Eltoft, T. (2018). Evaluation of feature ranking and regression methods for oceanic chlorophyll-a estimation. <i>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11</i>(5), 1403-1418. https://doi.org/10.1109/JSTARS.2018.2810704en_US
dc.identifier.cristinIDFRIDAID 1583174
dc.identifier.doi10.1109/JSTARS.2018.2810704
dc.identifier.issn1939-1404
dc.identifier.issn2151-1535
dc.identifier.urihttps://hdl.handle.net/10037/15028
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.ispartofBlix, K. (2019). Machine Learning Water Quality Monitoring. (Doctoral thesis). <a href=https://hdl.handle.net/10037/16502>https://hdl.handle.net/10037/16502</a>.
dc.relation.journalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectArcticen_US
dc.subjectenvironmental monitoringen_US
dc.subjectgaussian processesen_US
dc.subjectoptical imagingen_US
dc.subjectrankingen_US
dc.subjectregression analysisen_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430en_US
dc.titleEvaluation of feature ranking and regression methods for oceanic chlorophyll-a estimationen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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