TITLE:
Updated Lithological Map in the Forest Zone of the Centre, South and East Regions of Cameroon Using Multilayer Perceptron Neural Network and Landsat Images
AUTHORS:
Charlie Gael Atangana Otele, Mathias Akong Onabid, Patrick Stephane Assembe, Marcellin Nkenlifack
KEYWORDS:
Neural Network, Multilayer Perceptron, Principal Components Analysis, Independent Components Analysis, Lithological Classification, Geological Mapping
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.9 No.6,
June
22,
2021
ABSTRACT: 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).