Sub-pixel mapping with point constraints
文献类型：期刊论文
| 作者 | Wang, Qunming1; Zhang, Chengyuan1; Atkinson, Peter M.2,3,4 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2020-07-01 |
| 卷号 | 244页码:16 |
| 关键词 | Remote sensing images Sub-pixel mapping (SPM) Super-resolution mapping Downscaling Pixel swapping algorithm (PSA) Point constraints |
| ISSN号 | 0034-4257 |
| DOI | 10.1016/j.rse.2020.111817 |
| 通讯作者 | Wang, Qunming(wangqm@tongji.edu.cn) |
| 英文摘要 | Remote sensing images contain abundant land cover information. Due to the complex nature of land cover, however, mixed pixels exist widely in remote sensing images. Sub-pixel mapping (SPM) is a technique for predicting the spatial distribution of land cover classes within mixed pixels. As an ill-posed inverse problem, the uncertainty of prediction cannot be eliminated and hinders the production of accurate sub-pixel maps. In contrast to conventional methods that use continuous geospatial information (e.g., images) to enhance SPM, in this paper, a SPM method with point constraints into SPM is proposed. The method of fusing point constraints is implemented based on the pixel swapping algorithm (PSA) and utilizes the auxiliary point information to reduce the uncertainty in the SPM process and increase map accuracy. The point data are incorporated into both the initialization and optimization processes of PSA. Experiments were performed on three images to validate the proposed method. The influences of the performances were also investigated under different numbers of point data, different spatial characters of land cover and different zoom factors. The results show that by using the point data, the proposed SPM method can separate more small-sized targets from aggregated artifacts and the accuracies are increased obviously. The proposed method is also more accurate than the advanced radial basis function interpolation-based method. The advantage of using point data is more evident when the point data size and scale factor are large and the spatial autocorrelation of the land cover is small. As the amount of point data increases, however, the increase in accuracy becomes less noticeable. Furthermore, the SPM accuracy can still be increased even if the point data and coarse proportions contain errors. |
| WOS关键词 | HOPFIELD NEURAL-NETWORK ; REMOTELY-SENSED IMAGES ; URBAN LAND-USE ; SOFT CLASSIFICATION ; SPATIAL-RESOLUTION ; COVER ; REGRESSION ; SCALE ; IDENTIFICATION ; INFORMATION |
| 资助项目 | National Natural Science Foundation of China[41971297] ; Fundamental Research Funds for the Central Universities[02502150021] ; Tongji University[02502350047] |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000532837400010 |
| 出版者 | ELSEVIER SCIENCE INC |
| 资助机构 | National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Tongji University |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/159696]  |
| 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, 1239 Siping Rd, Shanghai 200092, Peoples R China 2.Univ Lancaster, Fac Sci & Technol, Lancaster LA1 4YR, England 3.Univ Southampton, Geog & Environm, Southampton SO17 1BJ, Hants, England 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Datun Rd, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Qunming,Zhang, Chengyuan,Atkinson, Peter M.. Sub-pixel mapping with point constraints[J]. REMOTE SENSING OF ENVIRONMENT,2020,244:16. |
| APA | Wang, Qunming,Zhang, Chengyuan,&Atkinson, Peter M..(2020).Sub-pixel mapping with point constraints.REMOTE SENSING OF ENVIRONMENT,244,16. |
| MLA | Wang, Qunming,et al."Sub-pixel mapping with point constraints".REMOTE SENSING OF ENVIRONMENT 244(2020):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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