Remote Sensing Based Vegetation Classification Using Machine Learning Algorithms

Abstract

Vegetation is one of the most important part of an ecosystem. It is responsible for providing oxygen and gets in carbon dioxide, hence providing a suitable place for the human beings to live. The information about this vegetation is very critical. Using remote sensing, this information can be taken and gathered and later on used for different purposes. This paper aims to classify vegetation into different types and categories. Three machine learning algorithms i.e. K-means, Support Vector Machine (SVM) and Artificial Neural Networks (ANN) have been used because of their being the most popular and well known algorithms of the current time to classify vegetation. K-means being unsupervised classifier is used to compare it to two supervised classifiers i.e. SVM and ANN. Non-vegetation including buildings, roads, rivers etc. are also classified into their respective categories. This classification can be useful in many ways. They can be used by government agencies and authorities to get information about the yield of a specific crop e.g. tobacco, maize etc. This information could be very useful for gathering statistics of the crop and its location on map. These locations can be used for extracting the crops and for future planning regarding it. The information about buildings and roads can help in town planning for future.