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- W3191734128 abstract "Remote sensing image classification has long attracted the attention of the remote‐sensing community because classification results are the basis for many environmental and socioeconomic applications. The classification involves a number of steps, one of the most important is the selection of an effective image classification technique. This paper provides a comparative study of the supervised learning techniques for remote sensing image classification. The study is being focused on classification of land cover and land use. Supervised learning is a branch of machine learning and is used in this study. The comparison is made among the different techniques of pixel-based supervised classification used for remote sensing image classification. The study has been made on a labelled data set. After the implementation, support vector machine has been found to be the most effective algorithm among the five algorithms of pixel-based supervised classification (i.e. maximum likelihood estimation, minimum distance classifier, principal component analysis, isoclustering and support vector machine)." @default.
- W3191734128 created "2021-08-16" @default.
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- W3191734128 date "2021-07-31" @default.
- W3191734128 modified "2023-10-16" @default.
- W3191734128 title "A Comparative Study of Supervised Learning Techniques for Remote Sensing Image Classification" @default.
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- W3191734128 doi "https://doi.org/10.1007/978-981-16-1740-9_6" @default.
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