Fast and Slow Changes Constrained Spatio-Temporal Subpixel Mapping
文献类型：期刊论文
| 作者 | Zhang, Chengyuan1; Wang, Qunming1; Lu, Ping1; Ge, Yong2; Atkinson, Peter M.3,4,5 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2022 |
| 卷号 | 60页码:16 |
| 关键词 | Spatial resolution Neurons Monitoring Image resolution Uncertainty Remote sensing Satellites Downscaling Hopfield neural network (HNN) land cover and land use (LCLU) spatio-temporal dependence subpixel mapping (SPM) super-resolution mapping |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2021.3133534 |
| 通讯作者 | Wang, Qunming(wqm11111@126.com) |
| 英文摘要 | Subpixel mapping (SPM) is a technique to tackle the mixed-pixel problem and produces land cover and land use (LCLU) maps at a finer spatial resolution than the original coarse data. However, uncertainty exists unavoidably in SPM, which is an ill-posed downscaling problem. Spatio-temporal SPM methods have been proposed to deal with this uncertainty, but current methods fail to explore fully the information in the time-series images, especially more rapid changes over a short-time interval. In this article, a fast and slow changes constrained spatio-temporal subpixel mapping (FSSTSPM) method is proposed to account for fast LCLU changes over a short time interval and slow changes over a long time interval. Both fast and slow changes-based temporal constraints are proposed and incorporated simultaneously into the FSSTSPM to increase the accuracy of SPM. The proposed FSSTSPM method was validated using two synthetic datasets with various proportion errors. It was also applied to oil-spill mapping using a real PlanetScope-Sentinel-2 dataset and Amazon deforestation mapping using a real Landsat-Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. The results demonstrate the superiority of FSSTSPM. Moreover, the advantage of FSSTSPM is more obvious with an increase in proportion errors. The concepts of the fast and slow changes, together with the derived temporal constraints, provide a new insight to enhance SPM by taking fuller advantage of the temporal information in the available time-series images. |
| WOS关键词 | REMOTELY-SENSED IMAGES ; LAND-COVER ; SCALE ; CLASSIFICATION ; PLANETSCOPE ; ALGORITHM ; ACCURACY |
| 资助项目 | National Natural Science Foundation of China[41971297] ; National Natural Science Foundation of China[42171345] ; Tongji University[02502350047] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000766298800014 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Tongji University |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/171666]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Qunming |
| 作者单位 | 1.Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Univ Lancaster, Fac Sci & Technol, Lancaster LA1 4YR, England 4.Univ Southampton, Geog & Environm, Southampton SO17 1BJ, Hants, England 5.Chinese Acad Sci, Beijing 100864, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Chengyuan,Wang, Qunming,Lu, Ping,et al. Fast and Slow Changes Constrained Spatio-Temporal Subpixel Mapping[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:16. |
| APA | Zhang, Chengyuan,Wang, Qunming,Lu, Ping,Ge, Yong,&Atkinson, Peter M..(2022).Fast and Slow Changes Constrained Spatio-Temporal Subpixel Mapping.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,16. |
| MLA | Zhang, Chengyuan,et al."Fast and Slow Changes Constrained Spatio-Temporal Subpixel Mapping".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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