Author(s)

Charlie Gael Atangana Otele^1,2, Mathias Akong Onabid^1,3*, Patrick Stephane Assembe⁴, Marcellin Nkenlifack¹

¹URIFIA, Department of Mathematics and Computer Science, Faculty of Science, University of Dschang, Dschang, Cameroon.
²Department of Computer Science, Higher Teacher Training College, The University of Bamenda, Bambili, Cameroon.
³Department of Computer Science, Higher Technical Teacher Training College, The University of Bamenda, Bambili, Cameroon.
⁴Postgraduate School of Science, Technology and Geoscience, Geophysics and Geo-Exploration Unit, University of Yaoundé-I, Yaoundé, Cameroon.

The Multilayer Perceptron Neural Network (MLPNN) induction technique has been successfully applied to a variety of machine learning tasks, including the extraction and classification of image features. However, not much has been done in the application of MLPNN on images obtained by remote sensing. In this article, two automatic classification systems used in image feature extraction and classification from remote sensing data are presented. The first is a combination of two models: a MLPNN induction technique, integrated under ENVI (Environment for Visualizing Images) platform for classification, and a pre-processing model including dark subtraction for the calibration of the image, the Principal Components Analysis (PCA) for band selections and Independent Components Analysis (ICA) as blind source separator for feature extraction of the Landsat image. The second classification system is a MLPNN induction technique based on the Keras platform. In this case, there was no need for pre-processing model. Experimental results show the two classification systems to outperform other typical feature extraction and classification methods in terms of accuracy for some lithological classes including Granite1 class with the highest class accuracies of 96.69% and 92.69% for the first and second classification system respectively. Meanwhile, the two classification systems perform almost equally with the overall accuracies of 53.01% and 49.98% for the first and second models respectively though the keras model has the advantage of not integrating the pre-processing model, hence increasing its efficiency. The application of these two systems to the study area resulted in the generation of an updated geological mapping with six lithological classes detected including the Gneiss, the Micaschist, the Schist and three versions of Granites (Granite1, Granite2 and Granite3).

Neural Network, Multilayer Perceptron, Principal Components Analysis, Independent Components Analysis, Lithological Classification, Geological Mapping

Atangana Otele, C. , Onabid, M. , Assembe, P. and Nkenlifack, M. (2021) Updated Lithological Map in the Forest Zone of the Centre, South and East Regions of Cameroon Using Multilayer Perceptron Neural Network and Landsat Images. Journal of Geoscience and Environment Protection, 9, 120-134. doi: 10.4236/gep.2021.96007.