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    Efficient land desertification detection using a deep learning-driven generative adversarial network approach: A case study

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    Name:
    CPE-21-0550R1-Clean.pdf
    Size:
    1.010Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Zerrouki, Nabil
    Dairi, Abdelkader cc
    Harrou, Fouzi cc
    Zerrouki, Yacine
    Sun, Ying cc
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Environmental Statistics Group
    Statistics Program
    KAUST Grant Number
    OSR-2019-CRG7-3800
    Date
    2021-09-12
    Online Publication Date
    2021-09-12
    Print Publication Date
    2022-02-15
    Embargo End Date
    2022-09-12
    Submitted Date
    2021-04-03
    Permanent link to this record
    http://hdl.handle.net/10754/671191
    
    Metadata
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    Abstract
    Precisely 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.
    Citation
    Zerrouki, 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
    Sponsors
    This 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.
    Publisher
    Wiley
    Journal
    Concurrency and Computation: Practice and Experience
    DOI
    10.1002/cpe.6604
    Additional Links
    https://onlinelibrary.wiley.com/doi/10.1002/cpe.6604
    ae974a485f413a2113503eed53cd6c53
    10.1002/cpe.6604
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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