TITLE:
Semi-Automatic Fracture Mapping Using Cellular Neural Networks Applied to ALOS PALSAR 2 Images of the Western Highlands of Cameroon
AUTHORS:
Valère-Carin Jofack Sokeng, Benjamin N’gounou Ngatcha, Fernand Koffi Kouame, Jean Homian Danumah, Lucette Akpa You
KEYWORDS:
Fracture Map, Lineament Mapping, Cellular Neural Networks, Highlands of Cameroon, ALOS PALSAR Image
JOURNAL NAME:
International Journal of Geosciences,
Vol.12 No.11,
November
30,
2021
ABSTRACT: In Cameroon in general and in the Highlands of
Cameroon in particular, there is no fracture map since its realization is not
easy. The region’s harsh accessibility and climatic conditions make it
difficult to carry out geological prospecting field missions that require large
investments. This study proposes a semi-automatic lineament mapping approach to
facilitate the elaboration of the fracture map in the West Cameroon Highlands.
It uses neural networks in tandem with PCI Geomatica’s LINE algorithm to
extract lineaments semi-automatically from an ALOS PALSAR 2 radar image. The
cellular neural network algorithm of Lepage et al (2000) is implemented to
enhance the pre-processed radar image. Then, the LINE module of Geomatica is
applied to the enhanced image for the automatic extraction of lineaments. Finally,
a control and a validation of the expert by spatial analysis allows elaborating the fracture map. The
results obtained show that neural networks enhance and facilitate the
identification of lineaments on the image. The resulting map contains more than
1800 fractures with major directions N20° - 30°, NS, N10° - 20°, N50° - 60°,
N70° - 80°, N80° - 90°, N100° - 110°, N110° - 120° and N130° - 140° and N140° -
150°. It can be very useful for geological and hydrogeological studies, and
especially to inform on the productivity of aquifers in this region of high
agro-pastoral and mining interest for Cameroon and the Central African
sub-region.