干旱区生态环境知识资源中心(Arid): High-resolution feature pyramid attention network for high spatial resolution images land-cover classification in arid oasis zones

Arid

DOI	10.1080/01431161.2024.2349266
	High-resolution feature pyramid attention network for high spatial resolution images land-cover classification in arid oasis zones
	Chen, Pengdi; Liu, Yong; Liu, Yi; Ren, Yuanrui; Zhang, Baoan; Gao, Xiaolong
通讯作者	Liu, Y
来源期刊	INTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN	0143-1161
EISSN	1366-5901
出版年	2024
卷号	45 期号:11 页码:3664-3688
英文摘要	Land-cover classification based on remote sensing technology has been adopted for decision-making concerning agricultural development, urban planning, and ecosystem protection in arid oasis zones. The semantic segmentation method based on deep learning, as a new paradigm, can effectively overcome the limitations of traditional pixel-based and object-based methods and obtain good classification results from high spatial resolution (HSR) remote sensing images. However, how to extract the exact category boundary and realize the high precision mapping is still a problem. This paper proposes a novel high-resolution feature pyramid attention network (HRFPANet) for land-cover classification. It effectively integrates the advantages of multi-scale feature extraction, attention mechanism, and feature fusion and alleviates boundary inconsistency, roughness, and category fragmentation associated with previous semantic segmentation models. The experimental results show that the mIoU score of HRFPANet is 79.5%, which is 11.5% and 2.6% higher than that of PSPNet and UPerNet, respectively. It proves the proposed model can be used for qualified land-cover mapping in arid oasis zones. Our source code is available at https://github.com/HPU-CPD/HRFPANet.git.
英文关键词	Arid oasis zones high spatial resolution image multi-scale semantic segmentation land-cover classification
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001229887400001
WOS关键词	REMOTE-SENSING IMAGES ; SEMANTIC SEGMENTATION ; NEURAL-NETWORK ; AWARE ; ROAD
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/2XILL650/404264
推荐引用方式 GB/T 7714	Chen, Pengdi,Liu, Yong,Liu, Yi,et al. High-resolution feature pyramid attention network for high spatial resolution images land-cover classification in arid oasis zones[J],2024,45(11):3664-3688.
APA	Chen, Pengdi,Liu, Yong,Liu, Yi,Ren, Yuanrui,Zhang, Baoan,&Gao, Xiaolong.(2024).High-resolution feature pyramid attention network for high spatial resolution images land-cover classification in arid oasis zones.INTERNATIONAL JOURNAL OF REMOTE SENSING,45(11),3664-3688.
MLA	Chen, Pengdi,et al."High-resolution feature pyramid attention network for high spatial resolution images land-cover classification in arid oasis zones".INTERNATIONAL JOURNAL OF REMOTE SENSING 45.11(2024):3664-3688.