Drzewiecki, 2017 - Google Patents
Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mappingDrzewiecki, 2017
View PDF- Document ID
- 14668121456979145332
- Author
- Drzewiecki W
- Publication year
- Publication venue
- Geodesy and Cartography
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Snippet
We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on …
- 238000010801 machine learning 0 title abstract description 41
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- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6261—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation partitioning the feature space
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