A Partition-Based Detection of Urban Villages Using High-Resolution Remote Sensing Imagery in Guangzhou, China
文献类型：期刊论文
| 作者 | Zhao, Lu1,2; Ren, Hongyan1; Cui, Cheng1,2; Huang, Yaohuan1,2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2020-07-01 |
| 卷号 | 12期号:14页码:19 |
| 关键词 | urban villages highly urbanized region partition strategy random forest high-resolution remote sensing images |
| DOI | 10.3390/rs12142334 |
| 通讯作者 | Ren, Hongyan(renhy@igsnrr.ac.cn) |
| 英文摘要 | High-resolution remotely sensed imageries have been widely employed to detect urban villages (UVs) in highly urbanized regions, especially in developing countries. However, the understanding of the potential impacts of spatially and temporally differentiated urban internal development on UV detection is still limited. In this study, a partition-strategy-based framework integrating the random forest (RF) model, object-based image analysis (OBIA) method, and high-resolution remote sensing images was proposed for the UV-detection model. In the core regions of Guangzhou, four original districts were re-divided into five new zones for the subsequent object-based RF-detection of UVs with a series features, according to the different proportion of construction lands. The results show that the proposed framework has a good performance on UV detection with an average overall accuracy of 90.23% and a kappa coefficient of 0.8. It also shows the possibility of transferring samples and models into a similar area. In summary, the partition strategy is a potential solution for the improvement of the UV-detection accuracy through high-resolution remote sensing images in Guangzhou. We suggest that the spatiotemporal process of urban construction land expansion should be comprehensively understood so as to ensure an efficient UV-detection in highly urbanized regions. This study can provide some meaningful clues for city managers identifying the UVs efficiently before devising and implementing their urban planning in the future. |
| WOS关键词 | CLASSIFICATION ; URBANIZATION ; SEGMENTATION ; TEXTURE ; SLUMS ; LAND |
| 资助项目 | National Natural Science Foundation of China[41571158] ; National Key Research and Development Program of China[2016YFC1302602] ; National Key Research and Development Program of China[2017YFB0503005] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000558762700001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/158098]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ren, Hongyan |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Lu,Ren, Hongyan,Cui, Cheng,et al. A Partition-Based Detection of Urban Villages Using High-Resolution Remote Sensing Imagery in Guangzhou, China[J]. REMOTE SENSING,2020,12(14):19. |
| APA | Zhao, Lu,Ren, Hongyan,Cui, Cheng,&Huang, Yaohuan.(2020).A Partition-Based Detection of Urban Villages Using High-Resolution Remote Sensing Imagery in Guangzhou, China.REMOTE SENSING,12(14),19. |
| MLA | Zhao, Lu,et al."A Partition-Based Detection of Urban Villages Using High-Resolution Remote Sensing Imagery in Guangzhou, China".REMOTE SENSING 12.14(2020):19. |
入库方式： OAI收割
来源：地理科学与资源研究所
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