干旱区生态环境知识资源中心(Arid): Methods of Sandy Land Detection in a Sparse-Vegetation Scene Based on the Fusion of HJ-2A Hyperspectral and GF-3 SAR Data

Arid

DOI	10.3390/rs14051203
	Methods of Sandy Land Detection in a Sparse-Vegetation Scene Based on the Fusion of HJ-2A Hyperspectral and GF-3 SAR Data
	Li, Yi; Wu, Junjun; Zhong, Bo; Shi, Xiaoliang; Xu, Kunpeng; Ao, Kai; Sun, Bin; Ding, Xiangyuan; Wang, Xinshuang; Liu, Qinhuo; Yang, Aixia; Chen, Fei; Shi, Mengqi
通讯作者	Wu, JJ
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:5
英文摘要	Accurate identification of sandy land plays an important role in sandy land prevention and control. It is difficult to identify the nature of sandy land due to vegetation covering the soil in the sandy area. Therefore, HJ-2A hyperspectral data and GF-3 Synthetic Aperture Radar (SAR) data were used as the main data sources in this article. The advantages of the spectral characteristics of a hyperspectral image and the penetration characteristics of SAR data were used synthetically to carry out mixed-pixel decomposition in the horizontal direction and polarization decomposition in the vertical direction. The results showed that in the study area of the Otingdag Sandy Land, in China, the accuracy of sandy land detection based on feature-level fusion and single GF-3 data was verified to be 92% in both cases by field data; the accuracy of sandy land detection based on feature-level fusion was verified to be 88.74% by the data collected from Google high-resolution imagery, which was higher than that based on single HJ-2A (74.17%) and single GF-3 data (88.08%). To further verify the universality of the feature-level fusion method for sandy land detection, Alxa sandy land was also used as a verification area and the accuracy of sandy land detection was verified to be as high as 88.74%. The method proposed in this paper made full use of the horizontal and vertical structural information of remote sensing data. The problem of mixed pixels in sparse-vegetation scenes in the horizontal direction and the problem of vegetation covering sandy soil in the vertical direction were both well solved. Accurate identification of sandy land can be realized effectively, which can provide technical support for sandy land prevention and control.
英文关键词	sandy land mixed pixel decomposition polarization decomposition support vector machine classification image fusion
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000769298700001
WOS关键词	REMOTE-SENSING DATA ; SOIL PROPERTIES ; DESERTIFICATION ; CLASSIFICATION ; PREDICTION ; DYNAMICS ; REGION ; MODEL
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; 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/2XILL650/394122
推荐引用方式 GB/T 7714	Li, Yi,Wu, Junjun,Zhong, Bo,et al. Methods of Sandy Land Detection in a Sparse-Vegetation Scene Based on the Fusion of HJ-2A Hyperspectral and GF-3 SAR Data[J],2022,14(5).
APA	Li, Yi.,Wu, Junjun.,Zhong, Bo.,Shi, Xiaoliang.,Xu, Kunpeng.,...&Shi, Mengqi.(2022).Methods of Sandy Land Detection in a Sparse-Vegetation Scene Based on the Fusion of HJ-2A Hyperspectral and GF-3 SAR Data.REMOTE SENSING,14(5).
MLA	Li, Yi,et al."Methods of Sandy Land Detection in a Sparse-Vegetation Scene Based on the Fusion of HJ-2A Hyperspectral and GF-3 SAR Data".REMOTE SENSING 14.5(2022).