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- W3126692022 abstract "Land use/cover information is fundamental for the sustainable management of resources. Notwithstanding the advancement of remote sensing, analysts daunt to generate sufficient-quality land use/cover products due to dense-cloud-contaminated and/or technical issues. This study proposes a novel approach (Ensemble Learning Updating Classifier/ELUC), which can be applied with various classification algorithms and data sets to simplistically generate new classifications or renew existing classifications with a remarkable accuracy improvement. Applying miscellaneous features of Landsat-8 images, the ELUC of a random-forest-based algorithm produces sequences of single-time classifications with a mean overall accuracy of 84%. Through the study period, these sequences of individual classifications were then joined to achieve a final classification which reaches an overall accuracy of 94%. Also, the ELUC of the random-forest-based algorithm outperforms that of Kernel-Density-Estimation with a 5% overall accuracy higher. These outcomes confirm the effectiveness of the ELUC for a remarkably consistent land use/cover estimation with a data-rich environment." @default.
- W3126692022 created "2021-02-15" @default.
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- W3126692022 date "2021-02-18" @default.
- W3126692022 modified "2023-09-23" @default.
- W3126692022 title "Ensemble learning updating classifier for accurate land cover assessment in tropical cloudy areas" @default.
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- W3126692022 doi "https://doi.org/10.1080/10106049.2021.1878292" @default.
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