Open AccessNDVI Classification using Supervised Learning Method
Author(s) -
L. Agilandeeswari,
Swathi S Shenoy S,
Priyadip Ray,
Prof. Rame Gowda M
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b7083.019320
Subject(s) - normalized difference vegetation index , land cover , computer science , machine learning , vegetation (pathology) , urbanization , quality (philosophy) , artificial intelligence , population , land use , index (typography) , remote sensing , geography , leaf area index , engineering , ecology , medicine , philosophy , civil engineering , demography , epistemology , pathology , sociology , world wide web , biology
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