Publications

@conference{Chairet2020458,

title = {Land cover classification of GeoEye image based on Convolutional Neural Networks},

author = {R. Chairet and Y. B. Salem and M. Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103000969\&doi=10.1109%2fSSD49366.2020.9364254\&partnerID=40\&md5=1297bae913d2f6f478cd0d36b65c75c8},

doi = {10.1109/SSD49366.2020.9364254},

isbn = {9781728110806},

year  = {2020},

date = {2020-01-01},

journal = {Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020},

pages = {458-461},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Land cover classification is an important topic in remote sensing. It provides useful information for environmental projects and ecosystem management. To succeed the mapping task, interest should be given to select a suitable classification algorithm. The recent advances in machine learning have shown the very great performances of the deep neural networks for many applications including the land cover mapping. The purpose of this work is to exploit the performance of Convolutional Neural Networks CNN to classify a GeoEye satellite image. Two CNN models with three and four convolution layers are tested to show the impact of the number of layers on the accuracy result. The two architectures outperform, with an overall accuracy more than 81%, the pixel based classification using Random Forest classifier. © 2020 IEEE.},

note = {cited By 2},

keywords = {Classification algorithm; Ecosystem management; Environmental projects; Land cover classification; Land cover mapping; Overall accuracies; Pixel based classifications; Random forest classifier, Convolution; Convolutional neural networks; Decision trees; Deep neural networks; Mapping; Remote sensing, Image classification},

pubstate = {published},

tppubtype = {conference}

}