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NGRDI-DCNLab: Integrating Spectral Prior and Deformable Convolution for Urban Green Space Extraction from High-Resolution RGB Remote Sensing Imagery

文献类型：期刊论文

作者Lin, Baoye3,4; Du, Xiaofeng3,4; Man, Wang3,4; Song, Zigeng3,4; Ren, Zhoupeng2; Nie, Qin3,4; Li, Zongmei3,4; Zhang, Xinchang1
刊名LAND
出版日期2026-03-17
卷号15期号:3页码:486
关键词urban green space extraction deep learning semantic segmentation RGB remote sensing imagery deformable convolutions Visible Vegetation Indices
DOI10.3390/land15030486
产权排序3
文献子类Article
英文摘要Accurate urban green space (UGS) mapping is essential for assessing urban ecosystem health and supporting sustainable development planning. However, deep learning-based UGS segmentation from Red-Green-Blue (RGB) remote sensing imagery faces two major challenges. First, the absence of near-infrared (NIR) information in RGB imagery hinders the ability to discriminate spectrally similar classes, such as vegetation and non-vegetation. Second, conventional convolutions with fixed receptive fields struggle to model the complex and irregular boundaries characteristic of UGS. To address these challenges, this study combined the Normalized Green-Red Difference Index with the Deformable Convolutional Network Lab (NGRDI-DCNLab) model, a semantic segmentation model tailored specifically for RGB-only imagery. Based on the DeepLabV3+ framework, the model introduced three core improvements: (1) The Normalized Green-Red Difference Index (NGRDI) was incorporated to compensate for the absence of NIR information, enhancing the spectral separability of vegetation pixels. (2) Standard convolutions in the decoder were replaced with deformable convolutions, enabling the network to more effectively adapt to irregular boundaries of UGS. (3) An NGRDI-weighted loss function was designed to assign higher weights to challenging samples and uncertain boundary regions, guiding the model toward more accurate edge delineation. Comprehensive evaluations on two public high-resolution datasets-the Wuhan Dense Labeling Dataset (WHDLD) and the Beijing subset of the Urban Green Space-1m dataset (UGS-1m_Beijing)-demonstrated that the NGRDI-DCNLab model outperformed existing popular deep learning models (like Unet++, etc.). Specifically, the deformable convolution effectively enhances the feature modeling capability for irregular boundaries; incorporating the NGRDI vegetation index as a fourth channel strengthens spectral feature representation and improves the distinction between vegetation and non-vegetation; and adding the dynamic NGRDI-weighted loss enables targeted learning for challenging samples. Through the synergistic effect of these three modules, the model achieves mean Intersection over Union (MIoU) scores of 84.77% and 77.66%, as well as F1-scores of 91.75% and 87.27%, on the WHDLD and UGS-1m_Beijing datasets, respectively. Furthermore, the model exhibited certain generalization capability on the unmanned aerial vehicle (UAV) dataset, the Urban Drone Dataset 6 (UDD6), attaining an MIoU of 87.43%. Our results confirm that high-precision UGS extraction is achievable using only RGB remote sensing imagery, providing a cost-effective and practical technical solution for refined urban governance and ecological monitoring.
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WOS关键词COVER
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001726470200001
出版者MDPI
源URL[http://ir.igsnrr.ac.cn/handle/311030/221503]  
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Man, Wang
作者单位1.Guangzhou Univ, Sch Geog Sci, Guangzhou 510006, Peoples R China
2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;
3.Key Lab Database & Parallel Comp Fujian Prov Xiame, Xiamen 361024, Peoples R China;
4.Xiamen Univ Technol, Coll Comp Sci & Technol, Xiamen 361024, Peoples R China;
推荐引用方式
GB/T 7714		 Lin, Baoye,Du, Xiaofeng,Man, Wang,et al. NGRDI-DCNLab: Integrating Spectral Prior and Deformable Convolution for Urban Green Space Extraction from High-Resolution RGB Remote Sensing Imagery[J]. LAND,2026,15(3):486.
APA Lin, Baoye.,Du, Xiaofeng.,Man, Wang.,Song, Zigeng.,Ren, Zhoupeng.,...&Zhang, Xinchang.(2026).NGRDI-DCNLab: Integrating Spectral Prior and Deformable Convolution for Urban Green Space Extraction from High-Resolution RGB Remote Sensing Imagery.LAND,15(3),486.
MLA Lin, Baoye,et al."NGRDI-DCNLab: Integrating Spectral Prior and Deformable Convolution for Urban Green Space Extraction from High-Resolution RGB Remote Sensing Imagery".LAND 15.3(2026):486.

入库方式： OAI收割

来源：地理科学与资源研究所

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