Sparse representation-based correlation analysis of non-stationary spatiotemporal big data
文献类型：期刊论文
| 作者 | Song, Weijing1; Liu, Peng1; Wang, Lizhe1 |
| 刊名 | INTERNATIONAL JOURNAL OF DIGITAL EARTH
 |
| 出版日期 | 2016 |
| 卷号 | 9期号:9页码:892-913 |
| 关键词 | C SAR DATA ARCHAEOLOGICAL PROSPECTION SPECKLE FILTERS LANDSCAPE XINJIANG MODELS CHINA GIS |
| 通讯作者 | Wang, LZ (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China. ; Wang, LZ (reprint author), China Univ Geosci, Sch Comp Sci, Wuhan, Peoples R China. |
| 英文摘要 | As the basic data of digital city and smart city research, Spatiotemporal series data contain rich geographic information. Alongside the accumulation of spatial time-series data, we are also encountering new challenges related to analyzing and mining the correlations among the data. Because the traditional methods of analysis also have their own suitable condition restrictions for the new features, we propose a new analytical framework based on sparse representation to describe the time, space, and spatial-time correlation. First, before analyzing the correlation, we discuss sparse representation based on the K-singular value decomposition (K-SVD) algorithm to ensure that the sparse coefficients are in the same sparse domain. We then present new computing methods to calculate the time, spatial, and spatial-time correlation coefficients in the sparse domain; we then discuss the functions' properties. Finally, we discuss change regulations for the gross domestic product (GDP), population, and Normalized Difference Vegetation Index (NDVI) spatial time-series data in China's Jing-Jin-Ji region to confirm the effectiveness and adaptability of the new methods. |
| 学科主题 | Physical Geography; Remote Sensing |
| 类目[WOS] | Geography, Physical ; Remote Sensing |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000382199700005 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39260]  |
| 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, Peoples R China 2.China Univ Geosci, Sch Comp Sci, Wuhan, Peoples R China 3.Univ Chinese Acad Sci, Sch Comp & Commun Engn, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Song, Weijing,Liu, Peng,Wang, Lizhe. Sparse representation-based correlation analysis of non-stationary spatiotemporal big data[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2016,9(9):892-913. |
| APA | Song, Weijing,Liu, Peng,&Wang, Lizhe.(2016).Sparse representation-based correlation analysis of non-stationary spatiotemporal big data.INTERNATIONAL JOURNAL OF DIGITAL EARTH,9(9),892-913. |
| MLA | Song, Weijing,et al."Sparse representation-based correlation analysis of non-stationary spatiotemporal big data".INTERNATIONAL JOURNAL OF DIGITAL EARTH 9.9(2016):892-913. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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