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).
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Joshi, A., Dhumka, A., Dhiman, Y., Rawat, C., Ritika (2022). A Comparative Study of Supervised Learning Techniques for Remote Sensing Image Classification. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-1740-9_6
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DOI: https://doi.org/10.1007/978-981-16-1740-9_6
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