干旱区生态环境知识资源中心(Arid): River Extraction from Remote Sensing Images in Cold and Arid Regions Based on Attention Mechanism

Arid

DOI	10.1155/2022/9410381
	River Extraction from Remote Sensing Images in Cold and Arid Regions Based on Attention Mechanism
	Wang, Hailong; Shen, Yu; Liang, Li; Yuan, Yubin; Yan, Yuan; Liu, Guanghui
通讯作者	Wang, HL
来源期刊	WIRELESS COMMUNICATIONS & MOBILE COMPUTING
ISSN	1530-8669
EISSN	1530-8677
出版年	2022
卷号	2022
英文摘要	The extraction of rivers in cold and arid regions is of great significance for applications such as ecological environment monitoring, agricultural planning, and disaster warning. However, there are few related studies on river extraction in cold and arid regions, and it is still in its infancy. The accuracy of river extraction is low, and the details are blurred. The rapid development of deep learning has provided us with new ideas, but with lack of corresponding professional datasets, the accuracy of the current semantic segmentation network is not high. This study mainly presents the following. (1) According to the characteristics of cold and arid regions, a professional dataset was made to support the extraction of rivers from remote sensing images in these regions. (2) Combine transfer learning and deep learning, migrate the ResNet-101 network to the LinkNet network, and introduce the attention mechanism to obtain the AR-LinkNet network, which is used to improve the recognition accuracy of the network. (3) A channel attention module and a spatial attention module with residual structure are proposed to strengthen the effective features and improve the segmentation accuracy. (4) Combining dense atrous spatial pyramid pooling (DenseASPP) with AR-LinkNet network expands the network receptive field, which can extract more detailed information and increase the coherence of extracted rivers. (5) For the first time, the binary cross-entropy loss function combined with the Dice loss function is applied to river extraction as a new loss function, which accelerates the network convergence and improves the image quality. Validation on the dataset shows that, compared with typical semantic segmentation networks, the method performs better on evaluation metrics such as recall, intersection ratio, precision, and F1 score, and the extracted rivers are clearer and more coherent.
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:000856041800016
WOS关键词	SEGMENTATION
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/2XILL650/394919
推荐引用方式 GB/T 7714	Wang, Hailong,Shen, Yu,Liang, Li,et al. River Extraction from Remote Sensing Images in Cold and Arid Regions Based on Attention Mechanism[J],2022,2022.
APA	Wang, Hailong,Shen, Yu,Liang, Li,Yuan, Yubin,Yan, Yuan,&Liu, Guanghui.(2022).River Extraction from Remote Sensing Images in Cold and Arid Regions Based on Attention Mechanism.WIRELESS COMMUNICATIONS & MOBILE COMPUTING,2022.
MLA	Wang, Hailong,et al."River Extraction from Remote Sensing Images in Cold and Arid Regions Based on Attention Mechanism".WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2022(2022).