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Appl. Sci. 2016, 6(11), 371; doi:10.3390/app6110371

Land Cover Classification Using a KOMPSAT-3A Multi-Spectral Satellite Image

Department of Civil Engineering, Kangwon National University, Chuncheon, Gangwon 24341, Korea
Author to whom correspondence should be addressed.
Academic Editor: Wen-Hsiang Hsieh
Received: 7 October 2016 / Revised: 15 November 2016 / Accepted: 16 November 2016 / Published: 21 November 2016
View Full-Text   |   Download PDF [15451 KB, uploaded 21 November 2016]   |  


New sets of satellite sensors are frequently being added to the constellation of remote sensing satellites. These new sets offer improved specification to collect imagery on-demand over specific locations and for specific purposes. The Korea Multi-Purpose Satellite (KOMPSAT) series of satellites is a multi-purposed satellite system developed by Korea Aerospace Research Institute (KARI). The recent satellite of the KOMPSAT series, KOMPSAT-3A, demonstrates high resolution multi-spectral imagery with infrared and high resolution electro-optical bands for geographical information systems applications in environmental, agricultural and oceanographic sciences as well as natural disasters. In this study, land cover classification of multispectral data was performed using four supervised classification methods: Mahalanobis Distance (MahD), Minimum Distance (MinD), Maximum Likelihood (ML) and Support Vector Machine (SVM), using a KOMPSAT-3A multi-spectral imagery with 2.2 m spatial resolution. The study area for this study was selected from southwestern region of South Korea, around Buan city. The training data for supervised classification was carefully selected by visual interpretation of KOMPSAT-3A imagery and field investigation. After classification, the results were then analyzed for the validation of classification accuracy by comparison with those of field investigation. For the validation, we calculated the User’s Accuracy (UA), Producer’s Accuracy (PA), Overall Accuracy (OA) and Kappa statistics from the error matrix to check the classification accuracy for each class obtained individually from different methods. Finally, the comparative analysis was done for the study area for various results of land cover classification using a KOMPSAT-3A multi-spectral imagery. View Full-Text
Keywords: land use; land cover; classification; KOMPSAT-3A; multi-spectral imagery; Mahalanobis Distance; Minimum Distance; Maximum Likelihood; Support Vector Machine land use; land cover; classification; KOMPSAT-3A; multi-spectral imagery; Mahalanobis Distance; Minimum Distance; Maximum Likelihood; Support Vector Machine

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Acharya, T.D.; Yang, I.T.; Lee, D.H. Land Cover Classification Using a KOMPSAT-3A Multi-Spectral Satellite Image. Appl. Sci. 2016, 6, 371.

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