Mapping Slums in Mumbai, India, Using Sentinel-2 Imagery: Evaluating Composite Slum Spectral Indices (CSSIs)
文献类型：期刊论文
| 作者 | Peng, Feifei1; Lu, Wei2,3; Hu, Yunfeng2,3; Jiang, Liangcun4 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-10-01 |
| 卷号 | 15期号:19页码:4671 |
| 关键词 | Sentinel-2 slum mapping CSSIs multispectral urban remote sensing Mumbai |
| DOI | 10.3390/rs15194671 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Accurate geographic data of slums are important for handling urban poverty issues. Previous slum mapping studies using high-resolution or very-high-resolution (HR/VHR) remotely sensed (RS) images are commonly not suitable for city-wide scale tasks. This study aims to efficiently generate a slum map on a city-wide scale using freely accessed multispectral medium-resolution (MR) Sentinel-2 images. Composite slum spectral indices (CSSIs) were initially proposed based on the shapes of spectral profiles of slums and nonslums and directly represent slum characteristics. Specifically, CSSI-1 denotes the normalized difference between the shortwave infrared bands and the red edge band, while CSSI-2 denotes the normalized difference between the blue band and the green band. Furthermore, two methods were developed to test the effectiveness of CSSIs on slum mapping, i.e., the threshold-based method and the machine learning (ML)-based method. Experimental results show that the threshold-based method and the ML-based method achieve intersection over unions (IoU) of 43.89% and 54.45% in Mumbai, respectively. The accuracies of our methods are comparable to or even higher than the accuracies reported by existing methods using HR/VHR images and transfer learning. The threshold-based method exhibits a promising performance in mapping slums larger than 5 ha, while the ML-based method refines mapping accuracies for slum pockets smaller than 5 ha. The threshold-based method and the ML-based method produced the slum map in Mumbai in 2 and 28 min, respectively. Our methods are suitable for rapid large-area slum mapping owing to the high data availability of Sentinel-2 images and high computational efficiency. |
| WOS关键词 | TEXTURE ; HYDERABAD ; ACCURACY ; AREA |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:001083361900001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/198866]  |
| 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.Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China 2.Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, 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.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 5.Wuhan Univ Technol, Sch Resources & Environm Engn, Wuhan 430070, Peoples R China |
| 推荐引用方式 GB/T 7714 | Peng, Feifei,Lu, Wei,Hu, Yunfeng,et al. Mapping Slums in Mumbai, India, Using Sentinel-2 Imagery: Evaluating Composite Slum Spectral Indices (CSSIs)[J]. REMOTE SENSING,2023,15(19):4671. |
| APA | Peng, Feifei,Lu, Wei,Hu, Yunfeng,&Jiang, Liangcun.(2023).Mapping Slums in Mumbai, India, Using Sentinel-2 Imagery: Evaluating Composite Slum Spectral Indices (CSSIs).REMOTE SENSING,15(19),4671. |
| MLA | Peng, Feifei,et al."Mapping Slums in Mumbai, India, Using Sentinel-2 Imagery: Evaluating Composite Slum Spectral Indices (CSSIs)".REMOTE SENSING 15.19(2023):4671. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。