干旱区生态环境知识资源中心(Arid): 3D-CNN based UAV hyperspectral imagery for grassland degradation indicator ground object classification research

Arid

DOI	10.1016/j.ecoinf.2021.101278
	3D-CNN based UAV hyperspectral imagery for grassland degradation indicator ground object classification research
	Pi, Weiqiang; Du, Jianmin; Bi, Yuge; Gao, Xinchao; Zhu, Xiangbing
通讯作者	Du, JM (corresponding author), Inner Mongolia Agr Univ, Mech & Elect Engn Coll, Hohhot, Peoples R China.
来源期刊	ECOLOGICAL INFORMATICS
ISSN	1574-9541
EISSN	1878-0512
出版年	2021
卷号	62
英文摘要	The identification and counting of grassland degradation indicator ground objects is an important component of grassland ecological monitoring. These steps are also an important basis for developing ecological restoration and management programs for degraded grasslands. Compared with a traditional human survey, the use of remote sensing images can not only achieve dynamic monitoring of a large area, but also improve the efficiency. Recently, most studies regarding ground object classification based on remote sensing images address the development and optimization of classification models for features in several widely used datasets. For the remote sensing of desertified grasslands, remote sensing images with high spatial resolutions are used for studies on small and sparse features in degraded grasslands. The spatial resolution of the above mentioned datasets yields difficulties when attempting to classify small and sparse indicator features for desertified grasslands because generalization becomes limited. Therefore, establishing a lightweight classification model suitable for degraded grassland features with high spatial resolution is important. In this study, a low altitude unmanned aerial vehicle (UAV) hyperspectral remote sensing platform was constructed to collect high spatial resolution remote sensing images of degraded grasslands. The GDIF-3D-CNN classification model was used to classify the pure pixels and all pixels datasets, whose accuracy and efficiency were further improved by optimizing the eight parameters of the model. This study explores the remote sensing ground object classification of thin small plants and a large number of mixed pixels, realizing high precision classification among desertification degradation indicating plant populations of a species, and provides key quantitative data for grassland degradation research.
英文关键词	3D convolutional neural networks Degraded grasslands Plant population classification UAV hyperspectral remote sensing
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000640475300001
WOS关键词	SATELLITE
WOS类目	Ecology
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350023
作者单位	[Pi, Weiqiang; Du, Jianmin; Bi, Yuge; Gao, Xinchao; Zhu, Xiangbing] Inner Mongolia Agr Univ, Mech & Elect Engn Coll, Hohhot, Peoples R China
推荐引用方式 GB/T 7714	Pi, Weiqiang,Du, Jianmin,Bi, Yuge,et al. 3D-CNN based UAV hyperspectral imagery for grassland degradation indicator ground object classification research[J],2021,62.
APA	Pi, Weiqiang,Du, Jianmin,Bi, Yuge,Gao, Xinchao,&Zhu, Xiangbing.(2021).3D-CNN based UAV hyperspectral imagery for grassland degradation indicator ground object classification research.ECOLOGICAL INFORMATICS,62.
MLA	Pi, Weiqiang,et al."3D-CNN based UAV hyperspectral imagery for grassland degradation indicator ground object classification research".ECOLOGICAL INFORMATICS 62(2021).