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- W4379929906 abstract "New spaceborne imaging spectrometers will provide great opportunities for mapping urban materials. We use the PRISMA Hyperspectral Imagery to estimate the fractional covers of key urban classes at two levels of details. Impervious, pervious and vegetation at level I where at level II we split the class Impervious to manmade surfaces (cement, asphalt, and metal roofs), the class pervious to soil and non-photosynthetic vegetation (NPV) and the class vegetation into and high and low canopy. The use of Random Forests Regression with synthetically mixed training data generated from the image-based spectral library was used for fraction mapping. Mean Absolute Error for all classes, at both levels, was lower than 18%. All classes at level I achieve error values close to 10% while at level II the majority achieves close to 11% with the lowest (5%) to be for the class metals and the highest (17%) to be at the class soil which has been overestimated across the urbanscape. We resume that the combination of RFR and synthetically mixed training data generated from the image-based library can allow a generalized urban fraction mapping across various cities of same typology and region when field-based data are absent." @default.
- W4379929906 created "2023-06-09" @default.
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- W4379929906 date "2023-05-17" @default.
- W4379929906 modified "2023-09-27" @default.
- W4379929906 title "Sub-pixel Material Fraction Mapping in the UrbanScape using PRISMA Hyperspectral Imagery" @default.
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- W4379929906 doi "https://doi.org/10.1109/jurse57346.2023.10144175" @default.
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