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dc.contributor.authorZerrouki, Nabil
dc.contributor.authorDairi, Abdelkader
dc.contributor.authorHarrou, Fouzi
dc.contributor.authorZerrouki, Yacine
dc.contributor.authorSun, Ying
dc.date.accessioned2021-09-13T10:45:08Z
dc.date.available2021-09-13T10:45:08Z
dc.date.issued2021-09-12
dc.date.submitted2021-04-03
dc.identifier.citationZerrouki, N., Dairi, A., Harrou, F., Zerrouki, Y., & Sun, Y. (2021). Efficient land desertification detection using a deep learning-driven generative adversarial network approach: A case study. Concurrency and Computation: Practice and Experience. doi:10.1002/cpe.6604
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.doi10.1002/cpe.6604
dc.identifier.urihttp://hdl.handle.net/10754/671191
dc.description.abstractPrecisely detecting land cover changes aids in improving the analysis of the dynamics of the landscape and plays an essential role in mitigating the effects of desertification. Mainly, sensing desertification is challenging due to the high correlation between desertification and like-desertification events (e.g., deforestation). An efficient and flexible deep learning approach is introduced to address desertification detection through Landsat imagery. Essentially, a generative adversarial network (GAN)-based desertification detector is designed and for uncovering the pixels influenced by land cover changes. In this study, the adopted features have been derived from multi-temporal images and incorporate multispectral information without considering image segmentation preprocessing. Furthermore, to address desertification detection challenges, the GAN-based detector is constructed based on desertification-free features and then employed to identify atypical events associated with desertification changes. The GAN-detection algorithm flexibly learns relevant information from linear and nonlinear processes without prior assumption on data distribution and significantly enhances the detection's accuracy. The GAN-based desertification detector's performance has been assessed via multi-temporal Landsat optical images from the arid area nearby Biskra in Algeria. This region is selected in this work because desertification phenomena heavily impact it. Compared to some state-of-the-art methods, including deep Boltzmann machine (DBM), deep belief network (DBN), convolutional neural network (CNN), as well as two ensemble models, namely, random forests and AdaBoost, the proposed GAN-based detector offers superior discrimination performance of deserted regions. Results show the promising potential of the proposed GAN-based method for the analysis and detection of desertification changes. Results also revealed that the GAN-driven desertification detection approach outperforms the state-of-the-art methods.
dc.description.sponsorshipThis work was supported by funding from King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No. OSR-2019-CRG7-3800.
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/10.1002/cpe.6604
dc.rightsArchived with thanks to Concurrency and Computation: Practice and Experience
dc.titleEfficient land desertification detection using a deep learning-driven generative adversarial network approach: A case study
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalConcurrency and Computation: Practice and Experience
dc.rights.embargodate2022-09-12
dc.eprint.versionPost-print
dc.contributor.institutionDesign and Implementation of Intelligent Machines (DIIM) Team Center for Development of Advanced Technologies Baba Hassen Algeria
dc.contributor.institutionLCPTS, Faculty of Electronics and Computer Science University of Sciences and Technology Houari Boumédienne Algiers Algeria
dc.contributor.institutionDepartment of Computer Science University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB) Bir El Djir Algeria
dc.contributor.institutionConservatoire National des Formations à l'Environnement Bab El Oued Algeria
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberOSR-2019-CRG7-3800
dc.date.accepted2021-08-10
refterms.dateFOA2021-09-13T11:36:33Z
kaust.acknowledged.supportUnitCRG
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


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