A Comparative Evaluation of Different Techniques of Supervised Classification in Landuse/Landcover Mapping of Awka South L.G.A, Anambra State, Nigeria.

Abstract

The aim of this study is to compare the different techniques of supervised classification using Awka South LGA, of Anambra State as a case study. The techniques considered include: Maximum Likelihood (MLC), Minimum Distance, Mahalanobis Distance, Spectral Angle Mapper and Parallelepiped. Landsat 7 ETM+ (2000 and 2007) and Landsat 8 OLI/TIRS (2015) were acquired. The images were pre-processed. The scan-line effect present in the Landsat 7 image was corrected using the analysis tool of Quantum GIS (QGIS) 2.18 software. To compensate for atmospheric effects, Fast Line-of-site Atmospheric Analysis of Hypercube (FLAASH) Atmospheric Module of ENVI software was used. Image enhancement was carried out on the images. The images were classified using the different techniques and the results compared. Change detection was also carried out to determine the rate of changes between 2000 and 2015. Error matrices of the various techniques were calculated to determine the accuracy level of the algorithms and to judge which is the better choice. It can be deduced from the results that Maximum Likelihood (99.63%) produced the best result, followed closely by Mahalanobis Distance (98.54%), Spectral Angle (89.28%), Minimum Distance (84.42%) and Parallelepiped (85.00%). The study recommends Maximum Likelihood Classification algorithm for supervised classification.

Key words: Classification, Maximum Likelihood, Algorithm, Land cover land use

DOI: 10.7176/JEES/9-5-10

Publication date:May 31^st 2019