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001-es BibID:BIBFORM105793
035-os BibID:(cikkazonosító)20919 (WoS)000969757300056 (Scopus)85143205656
Első szerző:Likó Szilárd Balázs
Cím:Tree species composition mapping with dimension reduction and post-classification using very high-resolution hyperspectral imaging / Szilárd Balázs Likó, László Bekő, Péter Burai, Imre J. Holb, Szilárd Szabó
Dátum:2022
ISSN:2045-2322
Megjegyzések:Tree species' composition of forests is essential in forest management and nature conservation. We aimed to identify the tree species structure of a floodplain forest area using a hyperspectral image. We proposed an efficient novel strategy including the testing of three dimension reduction (DR) methods: Principal Component Analysis, Minimum Noise Fraction (MNF) and Indipendent Component Analysis with five machine learning (ML) algorithms (Maximum Likelihood Classifier, Support Vector Classification, Support Vector Machine, Random Forest and Artificial Neural Network) to find the most accurate outcome; altogether 300 models were calculated. Post-classification was applied by combining the multiresolution segmentation and filtering. MNF was the most efficient DR technique, and at least 7 components were needed to gain an overall accuracy (OA) of?>?75%. Forty-five models had?>?80% OAs; MNF was 43, and the Maximum Likelihood was 19 times among these models. Best classification belonged to MNF with 10 components and Maximum Likelihood classifier with the OA of 83.3%. Post-classification increased the OA to 86.1%. We quantified the differences among the possible DR and ML methods, and found that even?>?10% worse model can be found using popular standard procedures related to the best results. Our workflow calls the attention of careful model selection to gain accurate maps.
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Megjelenés:Scientific Reports. - 12 : 12 (2022), p. 1-14. -
További szerzők:Bekő László (1986-) (okleveles vidékfejlesztési agrármérnök) Burai Péter (1994-) (informatikus) Holb Imre (1973-) (agrármérnök) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:TKP2020-NKA-04
Egyéb
2019-2.1.1-EUREKA-2019-00005
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NKFI-K-138079
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NKFI Co-operative Doctoral Program of the Ministry of Innovation and Technology
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