Abstract
The information from band extraction and calculation of indices which are used for classification imagery of Landsat 8 satellite data using unsupervised methods were studied. The visible and Near Infrared (NIR) bands of Landsat 8 satellite were used to derive Normalized Different Vegetation Index (NDVI) image. The Normalized Difference Water Index (NDWI) is a satellite-derived index from the NIR and Short Wave Infrared (SWIR) bands. Vegetation, non-vegetation, and water features classes were then analyzed by classification experiment of three unsupervised methods: ISODATA, K-means, and fuzzy c-means with guidance of ground truth information of the study area. The accuracy of the classified image is then assessed using a confusion matrix where classification accuracy and kappa coefficient are computed. The result shows that unsupervised methods classification is able to classify the Landsat 8 satellite data with FCM got a high accuracy compared to another two methods.
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Acknowledgements
The authors would like to thank the Universiti Teknikal Malaysia Melaka for funding the study through PJP/2018/FTK(16A)/S01642. Besides, thank you to the Faculty of Information Technology and Communication for providing excellent research facilities.
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Taufik, A., Syed Ahmad, S.S., Azmi, E.F. (2019). Classification of Landsat 8 Satellite Data Using Unsupervised Methods. In: Piuri, V., Balas, V., Borah, S., Syed Ahmad, S. (eds) Intelligent and Interactive Computing. Lecture Notes in Networks and Systems, vol 67. Springer, Singapore. https://doi.org/10.1007/978-981-13-6031-2_46
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DOI: https://doi.org/10.1007/978-981-13-6031-2_46
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