干旱区生态环境知识资源中心(Arid): Desertification Detection Using an Improved Variational Autoencoder-Based Approach Through ETM-Landsat Satellite Data

Arid

DOI	10.1109/JSTARS.2020.3042760
	Desertification Detection Using an Improved Variational Autoencoder-Based Approach Through ETM-Landsat Satellite Data
	Zerrouki, Yacine; Harrou, Fouzi; Zerrouki, Nabil; Dairi, Abdelkader; Sun, Ying
通讯作者	Harrou, F (corresponding author), King Abdullah Univ Sci & Technol, Comp Elect & Math Sci & Engn, Thuwal 23955, Saudi Arabia.
来源期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN	1939-1404
EISSN	2151-1535
出版年	2021
卷号	14 页码:202-213
英文摘要	The accurate land cover change detection is critical to improve the landscape dynamics analysis and mitigate desertification problems efficiently. Desertification detection is a challenging problem because of the high degree of similarity between some desertification cases and like-desertification phenomena, such as deforestation. This article provides an effective approach to detect deserted regions based on Landsat imagery and variational autoencoder (VAE). The VAE model, as a deep learning-based model, has gained special attention in features extraction and modeling due to its distribution-free assumptions and superior nonlinear approximation. Here, a VAE approach is applied to spectral signatures for detecting pixels affected by the land cover change. The considered features are extracted from multitemporal images and include multispectral information, and no prior image segmentation is required. The proposed method was evaluated on the publicly available remote sensing data using multitemporal Landsat optical images taken from the freely available Landsat program. The arid region around Biskra in Algeria is selected as a study area since it is well-known that desertification phenomena strongly influence this region. The VAE model was evaluated and compared with restricted Boltzmann machines, deep learning model, and binary clustering algorithms, including Agglomerative, BIRCH, expected maximization, k-mean clustering algorithms, and one-class support vector machine. The comparative results showed that the VAE consistently outperformed the other models for detecting changes to the land cover, mainly deserted regions. This study also showed that VAE outperformed the state-of-the-art algorithms.
英文关键词	Remote sensing Feature extraction Deep learning Earth Artificial satellites Vegetation mapping Indexes Desertification detection feature extraction Landsat sensors variational autoencoder (VAE) classification
类型	Article
语种	英语
开放获取类型	gold, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000607413900007
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/347944
作者单位	[Zerrouki, Yacine] Conservatoire Natl Format Environnement, Bab El Oued 16000, Algeria; [Harrou, Fouzi; Sun, Ying] King Abdullah Univ Sci & Technol, Comp Elect & Math Sci & Engn, Thuwal 23955, Saudi Arabia; [Zerrouki, Nabil] Ctr Dev Adv Technol, Baba Hassen 16081, Algeria; [Dairi, Abdelkader] Univ Sci & Technol Oran Mohamed Boudiaf, Dept Comp Sci, SIMPA Lab, Bir El Djir 31000, Algeria
推荐引用方式 GB/T 7714	Zerrouki, Yacine,Harrou, Fouzi,Zerrouki, Nabil,et al. Desertification Detection Using an Improved Variational Autoencoder-Based Approach Through ETM-Landsat Satellite Data[J],2021,14:202-213.
APA	Zerrouki, Yacine,Harrou, Fouzi,Zerrouki, Nabil,Dairi, Abdelkader,&Sun, Ying.(2021).Desertification Detection Using an Improved Variational Autoencoder-Based Approach Through ETM-Landsat Satellite Data.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,202-213.
MLA	Zerrouki, Yacine,et al."Desertification Detection Using an Improved Variational Autoencoder-Based Approach Through ETM-Landsat Satellite Data".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):202-213.