干旱区生态环境知识资源中心(Arid): Convolutional transformer attention network with few-shot learning for grassland degradation monitoring using UAV hyperspectral imagery

Arid

DOI	10.1080/01431161.2024.2326042
	Convolutional transformer attention network with few-shot learning for grassland degradation monitoring using UAV hyperspectral imagery
	Zhang, Tao; Bi, Yuge; Xuan, Chuanzhong
通讯作者	Bi, YG
来源期刊	INTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN	0143-1161
EISSN	1366-5901
出版年	2024
卷号	45 期号:6 页码:2109-2135
英文摘要	In recent years, the desertification of grasslands has increased due to various factors, including both global warming and human activities. It is an essential basis for grassland degradation monitoring to monitor the dynamic change of desert grassland vegetation communities and distribution statistics. Although unmanned aerial vehicle (UAV) remote sensing images have allowed us to achieve dynamic real-time grassland monitoring, the distribution of desert grassland ground objects can be random and narrow, thus increasing the difficulty of sample labelling of remote sensing imagery. Therefore, to reduce the number of samples required for the model, this research proposes a convolutional transformer attention network (CTAN) to identify desert grassland ground objects and validate it on a self-collected desert grassland dataset. The network utilizes the transformer model to enhance its learning of global pixels so that it suppresses the transmission of background pixels within the network. Furthermore, the edge convolution module is designed to strengthen the network's learning for edge pixels, improving its identification effect. The results show that the network provides 97.22% of overall accuracy (OA), 94.35% of average accuracy (AA), and 0.9398 of Kappa for ground object recognition in desert grassland. The model improves OA by 2.36-9.85% points compared to methods in the same field and 0.8-6.35% points compared to methods in hyperspectral imagery classification. The experimental results show the superior performance of the CTAN model for recognizing desert grassland objects, which helps the management and restoration of desert grasslands.
英文关键词	Desert steppe hyperspectral remote sensing unmanned aerial vehicle classification and identification few-shot learning
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001184031200001
WOS关键词	DESERT STEPPE ; INNER-MONGOLIA ; PLANT ; CLASSIFICATION ; VEGETATION ; DIVERSITY ; PATTERNS
WOS类目	Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	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/2XILL650/404263
推荐引用方式 GB/T 7714	Zhang, Tao,Bi, Yuge,Xuan, Chuanzhong. Convolutional transformer attention network with few-shot learning for grassland degradation monitoring using UAV hyperspectral imagery[J],2024,45(6):2109-2135.
APA	Zhang, Tao,Bi, Yuge,&Xuan, Chuanzhong.(2024).Convolutional transformer attention network with few-shot learning for grassland degradation monitoring using UAV hyperspectral imagery.INTERNATIONAL JOURNAL OF REMOTE SENSING,45(6),2109-2135.
MLA	Zhang, Tao,et al."Convolutional transformer attention network with few-shot learning for grassland degradation monitoring using UAV hyperspectral imagery".INTERNATIONAL JOURNAL OF REMOTE SENSING 45.6(2024):2109-2135.