NDVI Classification using Supervised Learning Method
Agilandeeswari L1, Swathi S Shenoy2, Paromita Ray3, Prabukumar M4
1Agilandeeswari L*, Associate Professor, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
2Swathi S Shenoy S, M.Tech (CCE), School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
3Paromita Ray, M.Tech (CCE), School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
4Prabukumar M, Associate Professor, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Manuscript received on December 16, 2019. | Revised Manuscript received on December 28, 2019. | Manuscript published on January 10, 2020. | PP: 721-728 | Volume-9 Issue-3, January 2020. | Retrieval Number: B7083129219/2020©BEIESP | DOI: 10.35940/ijitee.B7083.019320
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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: With the blessings of Science and Technology, as the death rate is getting decreased, population is getting increased. With that, the utilization of Land is also getting increased for urbanization for which the quality of Land is degrading day by day and also the climates as well as vegetations are getting affected. To keep the Land quality at its best possible, the study on Land cover images, which are acquired from satellites based on time series, spatial and colour, are required to understand how the Land can be used further in future. Using NDVI (Normalized Difference Vegetation Index) and Machine Learning algorithms (either supervised or unsupervised), now it is possible to classify areas and predict about Land utilization in future years. Our proposed study is to enhance the acquired images with better Vegetation Index which will segment and classify the data in more efficient way and by feeding these data to the Machine Learning algorithm model, higher accuracy will be achieved. Hence, a novel approach with proper model, Machine Learning algorithm and greater accuracy is always acceptable.
Keywords: Urbanisation, Vegetation Index, Machine Learning, Land Cover Images, Land Utilization, Classification, Prediction, NDVI.
Scope of the Article: Machine Learning