Efficient land desertification detection using a deep learning-driven generative adversarial network approach: A case study
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Science and Engineering (CEMSE) DivisionEnvironmental Statistics Group
Statistics Program
KAUST Grant Number
OSR-2019-CRG7-3800Date
2021-09-12Online Publication Date
2021-09-12Print Publication Date
2022-02-15Embargo End Date
2022-09-12Submitted Date
2021-04-03Permanent link to this record
http://hdl.handle.net/10754/671191
Metadata
Show full item recordAbstract
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.6604Sponsors
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
WileyDOI
10.1002/cpe.6604Additional Links
https://onlinelibrary.wiley.com/doi/10.1002/cpe.6604ae974a485f413a2113503eed53cd6c53
10.1002/cpe.6604