Terrain Mapping of LandSat8 Images using MNF and Classifying Soil Properties using Ensemble Modelling

Document Type : Research Paper


1 School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, India

2 Department of Computer Science, Faculty of Computer Science and Mathematics, University of Kufa, Iraq

3 School of Information Technology and Engineering, Vellore Institute of Technology Vellore, India

4 Department of Computer Science, National Institute of Technology, Patna, Patna-800005, India


Traditional technique for determining the soil texture and other soil properties is performed in laboratory which is a time consuming task. In this paper, machine learning algorithms are deployed to classify the soil texture and its properties without any intervention of laboratory equipment using the satellite images recorded by Landsat 8. These images are used to extract the terrain properties of the region which is integrated with weather data for the specific region and the vegetation index which are the major factors affecting the soil condition. A major aim of this paper is to design a robust technique for extracting, transforming Landsat images to numerical data and pre-processing the data for classifying the soil property. Minimum Noise Fraction (MNF) is utilized to segregate and remove noise from the Landsat images for subsequent processing. A significant amount of noise is present in the raw data which affects the accuracy of the analysis. Terrain features are extracted after noise removal from the MNF transformed images and merged with the weather data, and vegetation index for a period of time and then classified using voting classifier of the ensemble modeling or analysis of the soil texture of the region. The voting is performed by integrating the results of logistic regression, support vector machine and decision tree. With this study, the consolidated dependence of the soil texture on the environmental factors is analyzed and a cross validation accuracy of 94.44% is obtained.


Volume 11, Special Issue
November 2020
Pages 527-541
  • Receive Date: 08 February 2020
  • Revise Date: 12 October 2020
  • Accept Date: 14 October 2020