干旱区生态环境知识资源中心(Arid): Cloud Extraction Scheme for Multi-Spectral Images Using Landsat-8 OLI Images With High Brightness Reflectivity Covered

Arid

DOI	10.1109/ACCESS.2019.2962871
	Cloud Extraction Scheme for Multi-Spectral Images Using Landsat-8 OLI Images With High Brightness Reflectivity Covered
	Wu, Tingting 1; Han, Ling 1,2
通讯作者	Han, Ling
来源期刊	IEEE ACCESS
ISSN	2169-3536
出版年	2020
卷号	8 页码:3387-3396
英文摘要	Cloud extraction is a vital step in remote sensing image processing. Although many advanced cloud extraction methods have been proposed and confirmed to be effective in recent years, there are still difficulties in cloud extraction in areas of high brightness reflectivity covered. High brightness reflectivity cover can have similar spectral characteristics as clouds, and thus, it is easily confused with clouds in cloud extraction schemes. This work presents a novel scheme designed to extract clouds in satellite imagery with high brightness reflectivity covered. The fractal summation method and spatial analysis are used to extract the clouds in the Landsat 8 Operational Land Imager (OLI) images containing high brightness reflectivity covered. The scheme consists of three main steps: cloud extraction based on pixel values, Anselin Local Moran's I value, and anisotropy. Pixel values were applied to extract the clouds associated with anomalies, and the last two steps were conducted to eliminate false anomalies. The findings showed that the cloud-associated anomaly pixel-values well approximate a power-law function, but both the real and fake anomaly patches (e.g., snow/ice, desert, etc.) routinely coexist within the same (fractal) scaleless segments, and that the latter seems to be more significant than the former. Consequently, these results indicate that the diagnostic difference between true and false anomalies must lie in their spatial distribution patterns. Furthermore, experiments confirmed that the fractal dimension and spatial distribution (i.e. Anselin Local Moran's I index and anisotropy) difference between the real and false anomalies displayed a certain universality. The proposed scheme effectively reduces the confusion and misclassification caused by cloud, snow and the highlighted underlying surface. It is of great significance for cloud restoration processing, image analysis, image matching, target detection and extraction, and effective extraction and utilization of remote sensing data.
英文关键词	Cloud extraction spatial information fractal summation method Anselin Local Moran's I anisotropic analysis
类型	Article
语种	英语
国家	Peoples R China
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000549760300001
WOS关键词	ARTIFICIAL NEURAL-NETWORKS ; SNOW DETECTION ; SHADOW ; ALGORITHM ; DISCRIMINATION ; IMPROVEMENT ; REMOVAL
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS研究方向	Computer Science ; Engineering ; Telecommunications
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/319583
作者单位	1.Changan Univ, Sch Geol Engn & Geomat, Xian 710064, Peoples R China; 2.Changan Univ, Shaanxi Key Lab Land Consolidat & Rehabil, Xian 710064, Peoples R China
推荐引用方式 GB/T 7714	Wu, Tingting,Han, Ling. Cloud Extraction Scheme for Multi-Spectral Images Using Landsat-8 OLI Images With High Brightness Reflectivity Covered[J],2020,8:3387-3396.
APA	Wu, Tingting,&Han, Ling.(2020).Cloud Extraction Scheme for Multi-Spectral Images Using Landsat-8 OLI Images With High Brightness Reflectivity Covered.IEEE ACCESS,8,3387-3396.
MLA	Wu, Tingting,et al."Cloud Extraction Scheme for Multi-Spectral Images Using Landsat-8 OLI Images With High Brightness Reflectivity Covered".IEEE ACCESS 8(2020):3387-3396.