Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data
文献类型：期刊论文
| 作者 | Zhou, Yi1; Lin, Chenxi1; Wang, Shixin1; Liu, Wenliang1; Tian, Ye1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2016 |
| 卷号 | 8期号:11 |
| 关键词 | DRY SNOW GRAIN-SIZE CLIMATE-CHANGE RADAR RETRIEVAL SCATTERING MODEL POLARIZATION EMISSION SURFACES |
| 通讯作者 | Lin, CX (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China. ; Lin, CX (reprint author), Univ Chinese Acad Sci, Beijing 100049, Peoples R China. |
| 英文摘要 | Building density, as a component of impervious surface fraction, is a significant indicator of population distribution as essentially all humans live and conduct activities in buildings. Because population spatialization usually occurs over large areas, large-scale building density estimation through a proper, time-efficient, and relatively precise way is urgently required. Therefore, this study constructed a decision tree by the Classification and Regression Tree (CART) algorithm combining synthetic aperture radar (SAR) with optical images. The input features included four spectral bands (B1-4) of GF-1 PMS imagery; Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Ratio Built-up Index (RBI) derived from them; and backscatter intensity (BI) of Radarsat-2 SAR data. In addition, a new index called amended backscatter intensity (ABI), which takes the influence created by different spatial patterns into account, was introduced and calculated through fractal dimension and lacunarity. Result showed that before the integration use of multisource data, a model using B1-4, NDVI, NDWI, and RBI had the highest accuracy, with RMSE of 10.28 and R-2 of 0.63 for Jizhou and RMSE of 20.34 and R-2 of 0.36 for Beijing. In Comparison, the best model after combining two data sources (i.e., the model employing B1-4, NDVI, NDWI, RBI and ABI) reduced the RMSE to 8.93 and 16.21 raised the R-2 to 0.80 and 0.64, respectively. The result indicated that the synergistic use of optical and SAR data has the potential to improve the building density estimation performance and the addition of ABI has a better capacity for improving the model than other input features. |
| 学科主题 | Remote Sensing |
| 类目[WOS] | Remote Sensing |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000388798400089 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39222]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Yi,Lin, Chenxi,Wang, Shixin,et al. Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data[J]. REMOTE SENSING,2016,8(11). |
| APA | Zhou, Yi,Lin, Chenxi,Wang, Shixin,Liu, Wenliang,&Tian, Ye.(2016).Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data.REMOTE SENSING,8(11). |
| MLA | Zhou, Yi,et al."Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data".REMOTE SENSING 8.11(2016). |
入库方式： OAI收割
来源：遥感与数字地球研究所
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