Multi-scale Feature Fusion and Transformer Network for urban green space segmentation from high-resolution remote sensing images
文献类型：期刊论文
| 作者 | Cheng, Yong; Wang, Wei; Ren, Zhoupeng7; Zhao, Yingfen6; Liao, Yilan7; Ge, Yong4,5; Wang, Jun; He, Jiaxin; Gu, Yakang; Wang, Yixuan |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2023-11-01 |
| 卷号 | 124页码:103514 |
| 关键词 | Urban green space Deep learning Multi-scale feature fusion Vegetation feature High-resolution remote sensing images |
| DOI | 10.1016/j.jag.2023.103514 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Accurate extraction of urban green space is critical for preserving urban ecological balance and enhancing urban life quality. However, due to the complex urban green space morphology (e.g., different sizes and shapes), it is still challenging to extract green space effectively from high-resolution image. To address this issue, we proposed a novel hybrid method, Multi-scale Feature Fusion and Transformer Network (MFFTNet), as a new deep learning approach for extracting urban green space from high-resolution (GF-2) image. Our method was characterized by two aspects: (1) a multi-scale feature fusion module and transformer network that enhanced the recovery of green space edge information and (2) vegetation feature (NDVI) that highlighted vegetation information and enhanced vegetation boundaries identification. The GF-2 image was utilized to build two urban green space labeled datasets, namely Greenfield and Greenfield2. We compared the proposed MFFTNet with the existing popular deep learning models (like PSPNet, DensASPP, etc.) to evaluate the effectiveness of MFFTNet by the Mean Intersection Over Union (MIOU) benchmark on Greenfield, Greenfield2, and a public dataset (WHDLD). Experiments on Greenfield2 showed that MFFTNet can achieve a high MIOU (86.50%), which outperformed deep learning networks like PSPNet and DensASPP by 0.86% and 3.28%, respectively. Meanwhile, the MIOU of MFFTNet incorporating vegetation feature (NDVI) was further achieved to 86.76% on Greenfield2. Our experimental results demonstrate that the proposed MFFTNet with vegetation feature (NDVI) outperforms the state-ofthe-art methods in urban green space segmentation. |
| WOS关键词 | COVER ; MODIS |
| WOS研究方向 | Remote Sensing |
| WOS记录号 | WOS:001098939300001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/199455]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Univ Bristol, Sch Geog Sci, Bristol BS8 1SS, England 2.UK Ctr Ecol & Hydrol, Lib Ave, Lancaster LA1 4AP, England 3.Jiangxi Normal Univ, Sch Geog & Environm, Nanchang 330022, Peoples R China 4.Minist Educ, Key Lab Poyang Lake Wetland & Watershed Res, Nanchang 330022, Peoples R China 5.China Ctr Resources Satellite Data & Applicat, Beijing 100094, Peoples R China 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 7.Nanjing Univ Informat Sci & Technol, Nanjing 210044, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cheng, Yong,Wang, Wei,Ren, Zhoupeng,et al. Multi-scale Feature Fusion and Transformer Network for urban green space segmentation from high-resolution remote sensing images[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2023,124:103514. |
| APA | Cheng, Yong.,Wang, Wei.,Ren, Zhoupeng.,Zhao, Yingfen.,Liao, Yilan.,...&Zhang, Ce.(2023).Multi-scale Feature Fusion and Transformer Network for urban green space segmentation from high-resolution remote sensing images.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,124,103514. |
| MLA | Cheng, Yong,et al."Multi-scale Feature Fusion and Transformer Network for urban green space segmentation from high-resolution remote sensing images".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 124(2023):103514. |
入库方式： OAI收割
来源：地理科学与资源研究所
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