Performance Evaluation of Machine Learning Techniques on Rpas Remote Sensing Images
R. Madanamohana1, P Nagarjunapitty2, K.Rishitha3
1R. Madanamohana, Professor, Department of Computer Science and Engineering, Bharat Institute of Engineering and Technology, Hyderabad (Telangana), India.
2P Nagarjunapitty, Senior Scientific Officer, Indian Institute of Science, Bangalore (Karnataka), India.
3K. Rishitha, UG Scholar, Department of Computer Science and Engineering, Bharat Institute of Engineering and Technology, Hyderabad (Telangana), India.
Manuscript received on 18 August 2019 | Revised Manuscript received on 29 August 2019 | Manuscript Published on 06 September 2019 | PP: 1035-1039 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F11970886S19/19©BEIESP | DOI: 10.35940/ijeat.F1197.0886S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Remote Sensing, Machine Learning, R Software, Support Vector Machine, Random Forest, Classification and Regression Tree, Naive Bayes.
Scope of the Article: Machine Learning