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Mapping Invasive Plant Species with Hyperspectral Data Based on Iterative Accuracy Assessment Techniques

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dc.abstract.enRecent developments in computer hardware made it possible to assess the viability of permutation-based approaches in image classification. Such approaches sample a reference dataset multiple times in order to train an arbitrary number of machine learning models while assessing their accuracy. So-called iterative accuracy assessment techniques or Monte-Carlo-based approaches can be a useful tool when it comes to assessment of algorithm/model performance but are lacking when it comes to actual image classification and map creation. Due to the multitude of models trained, one has to somehow reason which one of them, if any, should be used in the creation of a map. This poses an interesting challenge since there is a clear disconnect between algorithm assessment and the act of map creation. Our work shows one of the ways this disconnect can be bridged. We calculate how often a given pixel was classified as given class in all variations of a multitude of post-classification images delivered by models trained during the iterative assessment procedure. As a classification problem, a mapping of Calamagrostis epigejos, Rubus spp., Solidago spp. invasive plant species using three HySpex hyperspectral datasets collected in June, August and September was used. As a classification algorithm, the support vector machine approach was chosen, with training hyperparameters obtained using a grid search approach. The resulting maps obtained F1-scores ranging from 0.87 to 0.89 for Calamagrostis epigejos, 0.89 to 0.97 for Rubus spp. and 0.99 for Solidago spp.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorSabat-Tomala, Anita
dc.contributor.authorRaczko, Edwin
dc.contributor.authorZagajewski, Bogdan
dc.date.accessioned2024-01-25T05:32:51Z
dc.date.available2024-01-25T05:32:51Z
dc.date.copyright2021-12-23
dc.date.issued2022
dc.description.accesstimeAT_PUBLICATION
dc.description.financePublikacja bezkosztowa
dc.description.number1
dc.description.versionFINAL_PUBLISHED
dc.description.volume14
dc.identifier.doi10.3390/RS14010064
dc.identifier.issn2072-4292
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/111805
dc.identifier.weblinkhttps://www.mdpi.com/2072-4292/14/1/64/pdf
dc.languageeng
dc.pbn.affiliationearth and related environmental sciences
dc.relation.ispartofRemote Sensing
dc.relation.pages64-83
dc.rightsCC-BY
dc.sciencecloudnosend
dc.subject.enimage classification
dc.subject.enmapping
dc.subject.enHySpex
dc.subject.enSVM
dc.titleMapping Invasive Plant Species with Hyperspectral Data Based on Iterative Accuracy Assessment Techniques
dc.typeJournalArticle
dspace.entity.typePublication