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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References
(2020) The USGS site [Online]. Available: https://www.usgs.gov/
Lu, D., Weng, Q.: “A survey of image classification methods and techniques for improving classification performance.” In: Int. J. Remote Sens., pp. 823–870 (Mar 2006)
Pradham, P., Younan, N.H., King, R.L.: “Concepts of image fusion in remote sensing applications.” Department of Electrical and Computer Engineering, Mississippi State University, USA
(2020) The GISgeography site [Online]. Available: https://gisgeography.com/spectral-signature/
Tuia, D., Volpi, M., Copa, L., Kanevski, M., Muñoz-Marí, J.: “A survey of active learning algorithms for supervised remote sensing ımage classification.” In: IEEE J. Sel. Top. Sign. Proces. 5(3) (Jun 2011)
Tuia, D., Ratle, F., Pacifici, F., Kanevski, M.F., Emery, W.J.: “Active learning methods for remote sensing ımage classification.” In: IEEE. Trans. Geosci. Remote Sens. 47(7) (Jul 2009)
Romero, A., Gatta, C., Camps-Valls, G.: “Unsupervised deep feature extraction for remote sensing ımage classification.” In: IEEE Trans. Geosci. Remote Sens. 54(3), 1349–1362 (Mar 2016). (2020) The Umetrics Suite Blogs Site [Online]. Available: https://blog.umetrics.com/what-is-principal-component-analysis-pca-and-how-it-is-used
(2020) The eurosat page on TensorFlow site [Online]. Available: https://www.tensorflow.org/datasets/catalog/eurosat
(2020) The GISgeography site [Online]. Available: https://gisgeography.com/image-classification-techniques-remote-sensing/
(2020) The Knowledge Portal on Stars Project sit [Online]. Available: https://www.stars-project.org/en/knowledgeportal/magazine/image-analysis/algorithmic-approaches/classification-approaches/pixel-based-classification/
(2020) The Esri Resources site [Online]. Available: https://resources.esri.com/help/9.3/arcgisengine/java/gp_toolref/spatial_aanalys_tools/how_maximum_likelihood_classification_works.htm
(2020) The Medium site [Online]. Available: https://medium.com/
(2020) The Analytics Vidhya site [Online]. Available: https://www.analyticsvidhya.com/blog/2018/07/introductory-guide-maximum-likelihood-estimation-case-study-r/
(2020) The Remote Sensing Lab. Available: https://sar.kangwon.ac.kr/
Jolliffe, I.T., Cadima, J.: “Principal component analysis: a review and recent developments.” Phil. Trans. R. Soc. A., 374, 20150202
(2020) The Esri Resources site [Online]. Available: https://resources.esri.com/help/9.3/arcgisdesktop/com/gp_toolref/spatial_analyst_tools/how_iso_cluster_works.htm
Rajpurohit, J., Sharma, T.K., Abraham, A., Vaishali.: “Glossary of metaheuristic algorithms.” In: Int. J. Comput. Inf. Syst. Indus. Manage. Appl. ISSN 2150-7988. 9, 181-205 (2017)
Sharma, T.K., Pant, M.: Opposition-based learning embedded shuffled frog-leaping algorithm. In: Pant, M., Ray, K., Sharma, T., Rawat, S., Bandyopadhyay, A. (eds.) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol. 583. Springer, Singapore, (2018). https://doi.org/10.1007/978-981-10-5687-1_76
Sharma, T.K., Rajpurohit, J., Prakash, D.: Enhanced local search in shuffled frog leaping algorithm. In: Pant, M., Sharma, T., Verma, O., Singla R., Sikander A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1053. Springer, Singapore, (2020). https://doi.org/10.1007/978-981-15-0751-9_132
Sharma, T.K., Sahoo, A.K., Goyal, P.: “Bidirectional butterfly optimization algorithm and engineering applications.” In: Materials Today: Proceedings. Doi: https://doi.org/10.1016/j.matpr.2020.04.679
Sharma, T.K., Rajpurohit, J., Sharma, V., Prakash, D.: Artificial bee colony application in cost optimization of project schedules in construction. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds.) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol. 742. Springer, Singapore, (2019). https://doi.org/10.1007/978-981-13-0589-4_63
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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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