Updated Lithological Map in the Forest Zone of the Centre, South and East Regions of Cameroon Using Multilayer Perceptron Neural Network and Landsat Images

ISSN Print: 2327-4336
ISSN Online: 2327-4344
www.scirp.org/journal/gep
E-mail: gep@scirp.org

Citations

"Updated Lithological Map in the Forest Zone of the Centre, South and East Regions of Cameroon Using Multilayer Perceptron Neural Network and Landsat Images"
written by Charlie Gael Atangana Otele, Mathias Akong Onabid, Patrick Stephane Assembe, Marcellin Nkenlifack,
published by Journal of Geoscience and Environment Protection, Vol.9 No.6, 2021

has been cited by the following article(s):

[1]	Design and implementation of an Automatic Deep Stacked Sparsely Connected Convolutional Autoencoder (ADSSCCA) neural network for remote sensing …
	Earth Science …, 2024

[2]	Design and Implementation of an Automatic Deep Stacked Sparsely Connected Auto-encoder (ADSSCA) Neural Network Architecture for Lithological Mapping under …
	2023

[3]	Lithology Mapping in Vegetation-Covered Regions: RS imagery, Method, Challenge and Opportunities
	2023

[4]	Design and Implementation of an Automatic Deep Stacked Sparsely Connected Auto-encoder (ADSSCA) Neural Network Architecture for Lithological Mapping …
	2023

[5]	Remote sensing for lithology mapping in vegetation-covered regions: methods, challenges, and opportunities
	Minerals, 2023

[6]	Machine learning algorithms for lithological mapping using Sentinel-2 and SRTM DEM in highly vegetated areas
	Frontiers in Ecology and …, 2023

[7]	A comparative study of convolutional neural networks and conventional machine learning models for lithological mapping using remote sensing data
	Remote Sensing, 2022

[8]	Automatic Evaluation Method of Oral English Based on Multiplex Mode
	Wireless Communications and Mobile Computing, 2022

[1]	Data Management, Analytics and Innovation Lecture Notes in Networks and Systems, 2026 DOI:10.1007/978-981-96-6534-1_19

[2]	Design and implementation of an Automatic Deep Stacked Sparsely Connected Convolutional Autoencoder (ADSSCCA) neural network for remote sensing lithological mapping using calculated dropout Earth Science Informatics, 2024 DOI:10.1007/s12145-024-01257-y

[3]	Machine learning algorithms for lithological mapping using Sentinel-2 and SRTM DEM in highly vegetated areas Frontiers in Ecology and Evolution, 2023 DOI:10.3389/fevo.2023.1250971

[4]	A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data Remote Sensing, 2022 DOI:10.3390/rs14040819

[5]	Automatic Evaluation Method of Oral English Based on Multiplex Mode Wireless Communications and Mobile Computing, 2022 DOI:10.1155/2022/9666524

Top