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GreenNet: A dual-encoder network for urban green space classification using high-resolution remotely sensed images

文献类型：期刊论文

作者Chen, Ke2; Wang, Yang3; Huang, Cunrui4; Wang, Jing6; Li, Sabrina L.1; Guan, Haiyan2; Ma, Lingfei5
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2025-08-01
卷号142页码:104709
关键词Green space classification High-resolution satellite images Dual-encoder structure Transformer Boundary loss
ISSN号1569-8432
DOI10.1016/j.jag.2025.104709
产权排序2
文献子类Article
英文摘要Accurate classification of urban green spaces from high-resolution remotely sensed images is critical for ecological environment planning, construction, and utilization. However, existing deep learning networks for large-scale high-resolution remote sensing images often face limited receptive fields and insufficient extraction of global information, making it challenging to achieve satisfactory performance on urban green space classification tasks. To address these issues, this paper presents a novel dual-encoder network, termed GreenNet, specifically designed for urban green space classification from high-resolution remotely sensed images. GreenNet features a unique dual-encoder structure. i.e., an inside encoder for efficiently extracting interior intra-image (i.e., local and global) features of urban green spaces from the small-sized images cropped from raw input remote sensing images, and an outside encoder for modeling long dependencies (i.e., external inter-image features) from the large-sized images cropped from raw input images. Additionally, a transformer-based outside-global-local attention block (OGLAB) is developed to fuse the intra-image and inter-image features from the dual-encoder to effectively capture inherent semantic representations of urban green spaces. Finally, to ensure classification consistency along class boundaries, a boundary loss is computed using edge-defined images, which are generated by a pre-trained Segmenting Anything Model (SAM) from the raw input image. The proposed GreenNet was evaluated on a self-built urban green space dataset, covering the whole area of Nanshan District, Shenzhen City, China, achieving an overall accuracy (OA) of 88.88 %, a mean F1-score (mF1) of 74.06 %, and a mean Intersection over Union (mIoU) of 60.77 %, respectively, demonstrating its superior performance to state-of-the-art networks on green space classification tasks.
URL标识查看原文
WOS关键词SEMANTIC SEGMENTATION
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:001530488300004
出版者ELSEVIER
源URL[http://ir.igsnrr.ac.cn/handle/311030/215310]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Huang, Cunrui; Wang, Jing
作者单位1.Univ Nottingham, Sch Geog, Nottingham NG7 2RD, England;
2.Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China;
3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;
4.Tsinghua Univ, Vanke Sch Publ Hlth, Beijing 100084, Peoples R China;
5.Cent Univ Finance & Econ, Sch Stat & Math, Beijing 102206, Peoples R China;
6.Jilin Normal Univ, Sch Geog Sci & Tourism, Siping 136000, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Ke,Wang, Yang,Huang, Cunrui,et al. GreenNet: A dual-encoder network for urban green space classification using high-resolution remotely sensed images[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2025,142:104709.
APA Chen, Ke.,Wang, Yang.,Huang, Cunrui.,Wang, Jing.,Li, Sabrina L..,...&Ma, Lingfei.(2025).GreenNet: A dual-encoder network for urban green space classification using high-resolution remotely sensed images.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,142,104709.
MLA Chen, Ke,et al."GreenNet: A dual-encoder network for urban green space classification using high-resolution remotely sensed images".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 142(2025):104709.

入库方式： OAI收割

来源：地理科学与资源研究所

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