干旱区生态环境知识资源中心(Arid): Efficient land desertification detection using a deep learning-driven generative adversarial network approach: A case study

Arid

DOI	10.1002/cpe.6604
	Efficient land desertification detection using a deep learning-driven generative adversarial network approach: A case study
	Zerrouki, Nabil; Dairi, Abdelkader; Harrou, Fouzi; Zerrouki, Yacine; Sun, Ying
通讯作者	Harrou, F (corresponding author), King Abdullah Univ Sci & Technol KAUST, Comp Elect & Math Sci & Engn CEMSE Div, Thuwal 239556900, Saudi Arabia.
来源期刊	CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
ISSN	1532-0626
EISSN	1532-0634
出版年	2021-09
英文摘要	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.
英文关键词	deep learning desertification detection generative adversarial networks land cover changes Landsat data
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000694991200001
WOS关键词	SENSITIVITY ; IMAGERY
WOS类目	Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/362900
作者单位	[Zerrouki, Nabil] Ctr Dev Adv Technol, Design & Implementat Intelligent Machines DIIM Te, Baba Hassen, Algeria; [Dairi, Abdelkader] Univ Sci & Technol Houari Boumedienne, LCPTS, Fac Elect & Comp Sci, Algiers, Algeria; [Dairi, Abdelkader] Univ Sci & Technol Oran Mohamed Boudiaf USTO MB, Dept Comp Sci, Bir El Djir, Algeria; [Harrou, Fouzi; Sun, Ying] King Abdullah Univ Sci & Technol KAUST, Comp Elect & Math Sci & Engn CEMSE Div, Thuwal 239556900, Saudi Arabia; [Zerrouki, Yacine] Conservatoire Natl Format Environm, Bab El Oued, Algeria
推荐引用方式 GB/T 7714	Zerrouki, Nabil,Dairi, Abdelkader,Harrou, Fouzi,et al. Efficient land desertification detection using a deep learning-driven generative adversarial network approach: A case study[J],2021.
APA	Zerrouki, Nabil,Dairi, Abdelkader,Harrou, Fouzi,Zerrouki, Yacine,&Sun, Ying.(2021).Efficient land desertification detection using a deep learning-driven generative adversarial network approach: A case study.CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE.
MLA	Zerrouki, Nabil,et al."Efficient land desertification detection using a deep learning-driven generative adversarial network approach: A case study".CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE (2021).