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CG-CFPANet: a multi-task network for built-up area extraction from SDGSAT-1 and Sentinel-2 remote sensing images

文献类型：期刊论文

作者Wang, Lei1,2,4; Ye, Cheng2; Chen, Fang1,4,5; Wang, Ning6; Li, Congrong1,3; Zhang, Haiying1,4; Wang, Yu7,8; Yu, Bo1,4
刊名INTERNATIONAL JOURNAL OF DIGITAL EARTH
出版日期2024-12-31
卷号17期号:1页码:19
ISSN号1753-8947
关键词Built-up area deep learning remote sensing image SDGSAT-1 semantic segmentation
DOI10.1080/17538947.2024.2310092
通讯作者Chen, Fang(chenfang@radi.ac.cn) ; Yu, Bo(yubo@radi.ac.cn)
英文摘要Accurate extraction of built-up areas is helpful to urban development and map updating. Nighttime light (NTL) data can capture the lighting signal of ground objects. However, most built-up area extraction is conducted on public limited coarse spatial resolution NTL images. The Sustainable Development Science Satellite-1 (SDGSAT-1) provides 10 m spatial resolution panchromatic NTL images, making it possible to map detailed urban lighting structures. In urban extraction, the boundaries of urban areas are easily confused with background objects due to the similar spectral and textual features. To address this problem, we proposed a multi-task deep learning model, CG-CFPANet, to extract illuminated built-up areas by synthesizing SDGSAT-1 NTL data and optical remote sensing images. In CG-CFPANet, a convolutional feature pyramid attention (CFPA) module for better contextual recognition and a concatenation group (CG) module to merge the two remote sensing images are developed. Our proposed CG-CFPANet achieved 1.3% higher precision in built-up area extraction than ten other recently proposed network structures: UNet, UNet++, PSPNet, DeeplabV3, FCN, ExtremeC3Net, SegNet, BiseNet, Res2-UNet, and CBRNet. It shows higher applicability for large-scale built-up area extraction.
WOS关键词NEURAL-NETWORK ; CONTEXTUAL INFORMATION ; SEMANTIC SEGMENTATION ; MODEL ; ALGORITHM
资助项目National Key R&D Program of China ; International Research Centre of Big Data for Sustainable Development Goals (CBAS) ; National Geomatics Center of China
WOS研究方向Physical Geography ; Remote Sensing
语种英语
出版者TAYLOR & FRANCIS LTD
WOS记录号WOS:001154180500001
资助机构National Key R&D Program of China ; International Research Centre of Big Data for Sustainable Development Goals (CBAS) ; National Geomatics Center of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/202573]  
专题中国科学院地理科学与资源研究所
通讯作者Chen, Fang; Yu, Bo
作者单位1.Int Res Ctr Big Data Sustainable Dev Goals, Beijing, Peoples R China
2.Guilin Univ Elect Technol, Sch Comp Sci & Informat Secur, Guilin, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
4.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing, Peoples R China
5.Univ Chinese Acad Sci, Beijing, Peoples R China
6.Peking Univ, Coll Urban & Environm Sci, Beijing, Peoples R China
7.Minist Ecol & Environm, Satellite Applicat Ctr Ecol & Environm, Beijing, Peoples R China
8.State Environm Protect Key Lab Satellite Remote Se, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Lei,Ye, Cheng,Chen, Fang,et al. CG-CFPANet: a multi-task network for built-up area extraction from SDGSAT-1 and Sentinel-2 remote sensing images[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2024,17(1):19.
APA Wang, Lei.,Ye, Cheng.,Chen, Fang.,Wang, Ning.,Li, Congrong.,...&Yu, Bo.(2024).CG-CFPANet: a multi-task network for built-up area extraction from SDGSAT-1 and Sentinel-2 remote sensing images.INTERNATIONAL JOURNAL OF DIGITAL EARTH,17(1),19.
MLA Wang, Lei,et al."CG-CFPANet: a multi-task network for built-up area extraction from SDGSAT-1 and Sentinel-2 remote sensing images".INTERNATIONAL JOURNAL OF DIGITAL EARTH 17.1(2024):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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