Effects of Training Samples and Classifiers on Classification of Landsat-8 Imagery
Sign Up to like & getrecommendations! Published in 2018 at "Journal of the Indian Society of Remote Sensing"
DOI: 10.1007/s12524-018-0777-z
Abstract: In this study, we used Landsat-8 imagery to test object- and pixel-based image classification approaches in an urban fringe area. For object-based classification, we applied four machine learning classifiers: decision tree (DT), naive Bayes (NB),… read more here.
Keywords: effects training; classification; pixel based; training samples ... See more keywords
From land cover to land use: Applying random forest classifier to Landsat imagery for urban land-use change mapping
Sign Up to like & getrecommendations! Published in 2021 at "Geocarto International"
DOI: 10.1080/10106049.2021.1923827
Abstract: The extensive record of Landsat imagery is commonly used to map urban land-cover and land-use change. Random forest (RF) classification was applied for mapping more detailed urban land-use and chan... read more here.
Keywords: urban land; land; landsat imagery; land use ... See more keywords