Landsat 8Bands’ 1 to 7 spectral vectors plus machine learning to improve land use/cover classification using Google Earth Engine

Landsat 8Bands’ 1 to 7 spectral vectors plus machine learning to improve land use/cover classification using Google Earth Engine

This paper explores a spectral vector-based methodology on Landsat 8 bands of the visible wavelengths, that is deep-blue (1) to shortwave infrared (7), to improve the urban land features classification. Using two different ratio models, based on two and three bands’ combinations in the cloud environ...

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Bibliographic Details
Main Authors: Alfred Homère Ngandam Mfondoum, Sofia Hakdaoui, Roseline Batcha
Format: Article
Language:English
Published: Taylor & Francis Group 2022-02-01
Series:Annals of GIS
Subjects:
spectral vector
machine learning classifiers
landsat8
google earth engine
hilly urban areas
Online Access:http://dx.doi.org/10.1080/19475683.2022.2026475

Internet

http://dx.doi.org/10.1080/19475683.2022.2026475

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