A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
文献类型：期刊论文
| 作者 | Wang, Wei2; Cheng, Yong3; Ren, Zhoupeng; He, Jiaxin2; Zhao, Yingfen5; Wang, Jun3; Zhang, Wenjie1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-12-01 |
| 卷号 | 15期号:23页码:5472 |
| 关键词 | urban green space deep learning high-resolution remote sensing images multiscale pooling attention feature engineering |
| DOI | 10.3390/rs15235472 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | The comprehensive use of high-resolution remote sensing (HRS) images and deep learning (DL) methods can be used to further accurate urban green space (UGS) mapping. However, in the process of UGS segmentation, most of the current DL methods focus on the improvement of the model structure and ignore the spectral information of HRS images. In this paper, a multiscale attention feature aggregation network (MAFANet) incorporating feature engineering was proposed to achieve segmentation of UGS from HRS images (GaoFen-2, GF-2). By constructing a new decoder block, a bilateral feature extraction module, and a multiscale pooling attention module, MAFANet enhanced the edge feature extraction of UGS and improved segmentation accuracy. By incorporating feature engineering, including false color image and the Normalized Difference Vegetation Index (NDVI), MAFANet further distinguished UGS boundaries. The UGS labeled datasets, i.e., UGS-1 and UGS-2, were built using GF-2. Meanwhile, comparison experiments with other DL methods are conducted on UGS-1 and UGS-2 to test the robustness of the MAFANet network. We found the mean Intersection over Union (MIOU) of the MAFANet network on the UGS-1 and UGS-2 datasets was 72.15% and 74.64%, respectively; outperforming other existing DL methods. In addition, by incorporating false color image in UGS-1, the MIOU of MAFANet was improved from 72.15% to 74.64%; by incorporating vegetation index (NDVI) in UGS-1, the MIOU of MAFANet was improved from 72.15% to 74.09%; and by incorporating false color image and the vegetation index (NDVI) in UGS-1, the MIOU of MAFANet was improved from 72.15% to 74.73%. Our experimental results demonstrated that the proposed MAFANet incorporating feature engineering (false color image and NDVI) outperforms the state-of-the-art (SOTA) methods in UGS segmentation, and the false color image feature is better than the vegetation index (NDVI) for enhancing green space information representation. This study provided a practical solution for UGS segmentation and promoted UGS mapping. |
| WOS关键词 | SEMANTIC SEGMENTATION ; CITIES |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001117991000001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200982]  |
| 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.China Ctr Resources Satellite Data & Applicat, Beijing 100094, 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.Nanjing Univ Informat Sci & Technol, Sch Automat, Nanjing 210044, Peoples R China 4.Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China 5.Nanjing Univ Informat Sci & Technol, Sch Software, Nanjing 210044, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Wei,Cheng, Yong,Ren, Zhoupeng,et al. A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images[J]. REMOTE SENSING,2023,15(23):5472. |
| APA | Wang, Wei.,Cheng, Yong.,Ren, Zhoupeng.,He, Jiaxin.,Zhao, Yingfen.,...&Zhang, Wenjie.(2023).A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images.REMOTE SENSING,15(23),5472. |
| MLA | Wang, Wei,et al."A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images".REMOTE SENSING 15.23(2023):5472. |
入库方式： OAI收割
来源：地理科学与资源研究所
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