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- W4377143275 abstract "There are several remotely sensed images of varied resolutions available. As a result, several classification techniques exist, which are roughly classified as pixel-based and object-based classification methods. Based on the foregoing, this study provided an integrated method of deriving land use from a coarse satellite image. Location coordinates of the land uses were acquired with a handheld Global Positioning System (GPS) instrument as primary data. The study classified the image quantitatively (pixel-based) into built-up, water, riparian, cultivated, and uncultivated land cover classes with no mixed pixels, and then qualitatively into educational, commercial, health, residential, and security land use classes that were conflicting due to spectral similarity. The total accuracy and kappa coefficient of the pixel-based land cover classification were 92.5% and 94% respectively. The results showed that residential land use occupied an area of 5500.01ha, followed by educational (2800.69ha); security (411.27ha); health (133.88ha); and commercial (109.01ha) respectively. The integrated method produces a crisp-appearance like the object-based image classification method. It eliminates the salt and pepper appearance that a traditional pixel-based classification would have. The output can be a vector or raster model depending on the purpose for which it is created." @default.
- W4377143275 created "2023-05-21" @default.
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- W4377143275 date "2023-06-15" @default.
- W4377143275 modified "2023-09-30" @default.
- W4377143275 title "Integrated Method for Classifying Medium Resolution Satellite Remotely Sensed Imagery into Land Use Map" @default.
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- W4377143275 doi "https://doi.org/10.30897/ijegeo.1150436" @default.
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