Performance Analysis of Different Classifier for Remote Sensing Application
Mahendra H N1, Mallikarjunaswamy S2, Rekha V3, Puspalatha V4, Sharmila N5
1Mahendra H N*, Assistant Professor/Research Scholar, Department of ECE, JSS Academy of Technical Education, Bangalore and Affiliated Visvesvaraya Technological University, Belagavi, Karnataka, India.
2Mallikarjunaswamy S, Associate Professor, Department of ECE, JSS Academy of Technical Education, Bangalore and Affiliated Visvesvaraya Technological University, Belagavi, Karnataka, India.
3Rekha V, Assistant Professor, Department of CSE, Christ University, Bangalore, India.
4Puspalatha V, Assistant Professor, Department of ISE, Mysore College of Engineering and Management, Mysore, India.
5Sharmila N, Assistant Professor, Department of EEE, RNSIT, Bangalore.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 7153-7158 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1879109119/2019©BEIESP | DOI: 10.35940/ijeat.A1879.109119
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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: The classification of remotely sensed data on thematic map is a challenging task from very long time and it is also a goal of today’s remote sensing because of complexity level of earth surface and selection of suitable classification technique. Hence selection of best classification technique in remote sensing will give better result. Classification of remotely sensed data is an important task within the domain of remote sensing and it is outlined as processing technique that uses a systematic approach to group the pixels into different classes. In this study, we have classified the multispectral data of Udupi district, Karnataka, India using different classifier including Support Vector Machine (SVM), Maximum Likelihood, Minimum Distance and Mahalanobis Distance classifier. The data of dimension 3980×3201 pixels are collected from a Landsat-3 satellite. Performance of the each classifier is compared by conducting accuracy assessment test and Kappa analysis. The obtained results shows that SVM will give accuracy of 95.35% and kappa value of 0.9408 respectively when compared other classifier, hence effectiveness of SVM is a good choice for classifying remotely sensed data.
Keywords: Remote Sensing, Support Vector Machines, Pixel-based, Multispectral data.