干旱区生态环境知识资源中心(Arid): Image Segmentation for Dust Detection Using Semi-supervised Machine Learning

Arid

DOI	10.1109/BigData50022.2020.9378198
	Image Segmentation for Dust Detection Using Semi-supervised Machine Learning
	Yu, Manzhu; Bessac, Julie; Xu, Ling; Gangopadhyay, Aryya; Shi, Yingxi; Wang, Jianwu
通讯作者	Yu, MZ (corresponding author), Penn State Univ, Dept Geog, University Pk, PA 16802 USA.
会议名称	8th IEEE International Conference on Big Data (Big Data)
会议日期	DEC 10-13, 2020
会议地点	ELECTR NETWORK
英文摘要	Dust plumes originating from the Earth's major arid and semi-arid areas can significantly affect the climate system and human health. Many existing methods have been developed to identify dust from non-dust pixels from a remote sensing point of view. However, these methods use empirical rules and therefore have difficulty detecting dust above or below the detectable thresholds. Supervised machine learning methods have also been applied to detect dust from satellite imagery, but these methods are limited especially when applying to areas outside the training data due to the inadequate amount of ground truth data. In this work, we proposed an automatic dust segmentation framework using semi-supervised machine learning, based on a collocated dataset using Visible Infrared Imaging Radiometer Suite (VIIRS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The proposed method utilizes unsupervised machine learning for segmentation of VIIRS imagery, and leverages the guidance from the dust labels using the dust profile product of CALIPSO to determine the dust clusters as the final product. The dust clusters are determined based on the similarity of spectral signature from dust pixels along the CALIPSO tracks. Experiment results show that the accuracy of the proposed framework outperforms the traditional physical infrared method along CALIPSO tracks. In addition, the proposed method performs consistently over three different study areas, the North Atlantic Ocean, East Asia, and Northern Africa.
英文关键词	dust detection semi-supervised machine learning multi-sensor remote sensing image segmentation
来源出版物	2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
ISSN	2639-1589
出版年	2020
页码	1745-1754
ISBN	978-1-7281-6251-5
出版者	IEEE
类型	Proceedings Paper
语种	英语
收录类别	CPCI-S
WOS记录号	WOS:000662554701103
WOS关键词	VOLCANIC ASH ; STORMS ; MODIS
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Theory & Methods
WOS研究方向	Computer Science
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/365535
作者单位	[Yu, Manzhu] Penn State Univ, Dept Geog, University Pk, PA 16802 USA; [Bessac, Julie] Argonne Natl Lab, Math & Comp Sci Div, Lemont, IL USA; [Xu, Ling] North Carolina A&T State Univ, Dept Math, Greensboro, NC USA; [Gangopadhyay, Aryya; Wang, Jianwu] Univ Maryland Baltimore Cty, Dept Informat Syst, Baltimore, MD 21228 USA; [Shi, Yingxi] Univ Maryland Baltimore Cty, Joint Ctr Earth Syst Technol, Baltimore, MD 21228 USA
推荐引用方式 GB/T 7714	Yu, Manzhu,Bessac, Julie,Xu, Ling,et al. Image Segmentation for Dust Detection Using Semi-supervised Machine Learning[C]:IEEE,2020:1745-1754.