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Performance evaluation of machine learning techniques on rpas remote sensing images

Madanamohana, R and Nagarjunapitty, P and Rishitha, K (2019) Performance evaluation of machine learning techniques on rpas remote sensing images. In: International Journal of Engineering and Advanced Technology, 8 (6 Spec). pp. 1035-1039.

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Official URL: https://doi.org/10.35940/ijeat.F1197.0886S19


Recent advancements in remote sensing platforms from satellites to close-range Remotely Piloted Aircraft System (RPAS), is principal to a growing demand for innovative image processing and classification tools. Where, Machine learning approaches are very prevailing group of data driven implication tools that provide a broader scope when applied to remote sensed data. In this paper, applying different machine learning approaches on the remote sensing images with open source packages in R, to find out which algorithm is more efficient for obtaining better accuracy. We carried out a rigorous comparison of four machine learning algorithms-Support vector machine, Random forest, regression tree, Classification and Naive Bayes. These algorithms are evaluated by Classification accurateness, Kappa index and curve area as accuracy metrics. Ten runs are done to obtain the variance in the results on the training set. Using k-fold cross validation the validation is carried out. This theme identifies Random forest approach as the best method based on the accuracy measure under different conditions. Random forest is used to train efficient and highly stable with respect to variations in classification representation parameter values and significantly more accurate than other machine learning approaches trailed.

Item Type: Journal Article
Publication: International Journal of Engineering and Advanced Technology
Publisher: Blue Eyes Intelligence Engineering and Sciences Publication
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Classification and Regression Tree; Machine Learning; Naive Bayes; R software; Random Forest; Remote Sensing; Support Vector Machine
Department/Centre: Centres under the Director > J.R.D.Tata Memorial Library
Date Deposited: 20 Oct 2022 12:01
Last Modified: 20 Oct 2022 12:01
URI: https://eprints.iisc.ac.in/id/eprint/77478

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