干旱区生态环境知识资源中心(Arid): Research on deep learning method recognition and a classification model of grassland grass species based on unmanned aerial vehicle hyperspectral remote sensing

Arid

DOI	10.1111/grs.12379
	Research on deep learning method recognition and a classification model of grassland grass species based on unmanned aerial vehicle hyperspectral remote sensing
	Zhu, Xiangbing; Bi, Yuge; Du, Jianmin; Gao, Xinchao; Zhang, Tao; Pi, Weiqiang; Zhang, Yanbin; Wang, Yuan; Zhang, Haijun
通讯作者	Bi, YG
来源期刊	GRASSLAND SCIENCE
ISSN	1744-6961
EISSN	1744-697X
出版年	2023
卷号	69 期号:1 页码:3-11
英文摘要	Identifying grass species in grasslands based on unmanned aerial vehicle hyperspectral remote sensing is the basis and premise of hyperspectral remote sensing when applied to grassland degradation monitoring and research. The small targets and mixed pixels involved grass species identification in grasslands creates problems, making identification cumbersome and classification accuracy difficult. This study involved the construction of an unmanned aerial vehicle hyperspectral remote sensing system using hyperspectral data of grass species in desert habitats that had been collected under natural light. A multi-resolution combined with a 1 x 1 feature map was formed by multiscale convolution, and grass species data were extracted from hyperspectral fine-grained feature data from grasslands. A recognition and classification model for degradation indicator species CNN was constructed using max pooling to retain the maximum amount of feature detail and up-sampling, reconstructing the feature space and feature fusion to smooth the edge texture of the data and enhance the weak data to alleviate the imbalance among samples. The results showed that the overall identification accuracy of the model for grassland species reached 98.78%, and the kappa coefficient reached 0.92, realizing the highprecision identification of grassland species, which laid the foundation for grassland species detection and research based on unmanned aerial vehicle hyperspectral imagery. In addition, the proposed degradation indicator species CNN model provides a useful reference for the identification and classification of small targets with mixed pixels.
英文关键词	grassland desertification hyperspectral image object identification UAV
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000837002800001
WOS关键词	QUANTITATIVE-ANALYSIS ; IMAGE CLASSIFICATION ; INDEX
WOS类目	Agriculture, Multidisciplinary ; Agronomy
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396794
推荐引用方式 GB/T 7714	Zhu, Xiangbing,Bi, Yuge,Du, Jianmin,et al. Research on deep learning method recognition and a classification model of grassland grass species based on unmanned aerial vehicle hyperspectral remote sensing[J],2023,69(1):3-11.
APA	Zhu, Xiangbing.,Bi, Yuge.,Du, Jianmin.,Gao, Xinchao.,Zhang, Tao.,...&Zhang, Haijun.(2023).Research on deep learning method recognition and a classification model of grassland grass species based on unmanned aerial vehicle hyperspectral remote sensing.GRASSLAND SCIENCE,69(1),3-11.
MLA	Zhu, Xiangbing,et al."Research on deep learning method recognition and a classification model of grassland grass species based on unmanned aerial vehicle hyperspectral remote sensing".GRASSLAND SCIENCE 69.1(2023):3-11.