干旱区生态环境知识资源中心(Arid): Desertification Detection in Makkah Region based on Aerial Images Classification

Arid

DOI	10.32604/csse.2022.018479
	Desertification Detection in Makkah Region based on Aerial Images Classification
	Said, Yahia; Barr, Mohammad; Saidani, Taoufik; Atri, Mohamed
通讯作者	Said, Y (corresponding author), Northern Border Univ, Coll Engn, Elect Engn Dept, Ar Ar, Saudi Arabia. ; Said, Y (corresponding author), Univ Monastir, Fac Sci Monastir, Lab Elect & Microelect LR99ES30, Monastir, Tunisia.
来源期刊	COMPUTER SYSTEMS SCIENCE AND ENGINEERING
ISSN	0267-6192
出版年	2022
卷号	40 期号:2 页码:607-618
英文摘要	Desertification has become a global threat and caused a crisis, especially in Middle Eastern countries, such as Saudi Arabia. Makkah is one of the most important cities in Saudi Arabia that needs to be protected from desertifica-tion. The vegetation area in Makkah has been damaged because of desertification through wind, floods, overgrazing, and global climate change. The damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation area. In this paper, we propose an automatic desertification detection system based on Deep Learning techniques. Aerial images are classified using Convolutional Neural Networks (CNN) to detect land state variation in real-time. CNNs have been widely used for computer vision applications, such as image classification, image segmentation, and quality enhancement. The proposed CNN model was trained and evaluated on the Arial Image Dataset (AID). Compared to state-of-the-art methods, the proposed model has better performance while being suitable for embedded implementation. It has achieved high efficiency with 96.47% accuracy. In light of the current research, we assert the appropriateness of the proposed CNN model in detecting desertifi- cation from aerial images.
英文关键词	Desertification detection deep learning convolutional neural networks (CNN) aerial images classification Makkah region
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000696953000014
WOS类目	Computer Science, Hardware & Architecture ; 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/2XILL650/362895
作者单位	[Said, Yahia; Barr, Mohammad] Northern Border Univ, Coll Engn, Elect Engn Dept, Ar Ar, Saudi Arabia; [Said, Yahia; Saidani, Taoufik; Atri, Mohamed] Univ Monastir, Fac Sci Monastir, Lab Elect & Microelect LR99ES30, Monastir, Tunisia; [Saidani, Taoufik] Northern Border Univ, Fac Comp & Informat Technol, Dept Comp Sci, Rafha, Saudi Arabia; [Atri, Mohamed] King Khalid Univ, Coll Comp Sci, Abha, Saudi Arabia
推荐引用方式 GB/T 7714	Said, Yahia,Barr, Mohammad,Saidani, Taoufik,et al. Desertification Detection in Makkah Region based on Aerial Images Classification[J],2022,40(2):607-618.
APA	Said, Yahia,Barr, Mohammad,Saidani, Taoufik,&Atri, Mohamed.(2022).Desertification Detection in Makkah Region based on Aerial Images Classification.COMPUTER SYSTEMS SCIENCE AND ENGINEERING,40(2),607-618.
MLA	Said, Yahia,et al."Desertification Detection in Makkah Region based on Aerial Images Classification".COMPUTER SYSTEMS SCIENCE AND ENGINEERING 40.2(2022):607-618.