干旱区生态环境知识资源中心(Arid): RAU-Net plus plus : River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions

Arid

DOI	10.3390/app14010251
	RAU-Net plus plus : River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions
	Tang, Yiyu; Zhang, Jianjie; Jiang, Zhangzhen; Lin, Ying; Hou, Peng
通讯作者	Zhang, JJ
来源期刊	APPLIED SCIENCES-BASEL
EISSN	2076-3417
出版年	2024
卷号	14 期号:1
英文摘要	Extracting river channels from remote sensing images is crucial for locating river water bodies and efficiently managing water resources, especially in cold and arid regions. The dynamic nature of river channels in these regions during the flood season necessitates a method that can finely delineate the edges of perennially changing river channels and accurately capture information about variable fine river branches. To address this need, we propose a river channel extraction method designed specifically for detecting fine river branches in remote sensing images within cold and arid regions. The method introduces a novel river attention U-shaped network structure (RAU-Net++), leveraging the rich convolutional features of VGG16 for effective feature extraction. For optimal feature extraction along channel edges and fine river branches, we incorporate a CBAM attention module into the upper sampling area at the end of the encoder. Additionally, a residual attention feature fusion module (RAFF) is embedded at each short jump connection in the dense jump connection. Dense skip connections play a crucial role in extracting detailed texture features from river channel features with varying receptive fields obtained during the downsampling process. The integration of the RAFF module mitigates the loss of river information, optimizing the extraction of lost river detail feature information in the original dense jump connection. This tightens the combination between the detailed texture features of the river and the high-level semantic features. To enhance network performance and reduce pixel-level segmentation errors in medium-resolution remote sensing imagery, we employ a weighted loss function comprising cross-entropy (CE) loss, dice loss, focal loss, and Jaccard loss. The RAU-Net++ demonstrates impressive performance metrics, with precision, IOU, recall, and F1 scores reaching 99.78%, 99.39%, 99.71%, and 99.75%, respectively. Meanwhile, both ED and ED ' of the RAU-Net++ are optimal, with values of 1.411 and 0.003, respectively. Moreover, its effectiveness has been validated on NWPU-RESISC45 datasets. Experimental results conclusively demonstrate the superiority of the proposed network over existing mainstream methods.
英文关键词	river channel segmentation convolutional neural network attention model remote sense
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:001139336200001
WOS关键词	SEGMENTATION ; WATER
WOS类目	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/402866
推荐引用方式 GB/T 7714	Tang, Yiyu,Zhang, Jianjie,Jiang, Zhangzhen,et al. RAU-Net plus plus : River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions[J],2024,14(1).
APA	Tang, Yiyu,Zhang, Jianjie,Jiang, Zhangzhen,Lin, Ying,&Hou, Peng.(2024).RAU-Net plus plus : River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions.APPLIED SCIENCES-BASEL,14(1).
MLA	Tang, Yiyu,et al."RAU-Net plus plus : River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions".APPLIED SCIENCES-BASEL 14.1(2024).