Sub-pixel land-cover mapping with improved fraction images upon multiple-point simulation
文献类型：SCI/SSCI论文
| 作者 | Ge Y. |
| 发表日期 | 2013 |
| 关键词 | Soft classification Uncertainty Sub-pixel mapping Multiple-point simulation remotely-sensed images linear mixture model soft classification spatial-resolution neural-network objects |
| 英文摘要 | Outputs of soft classification inherently contain uncertainty. As an input for the sub-pixel mapping (SPM) method, the uncertainty is propagated to SPM result especially the boundary region between classes. Therefore, reducing the uncertainty within the outputs of soft classification is worth exploring. This paper firstly utilizes multiple-point simulation (MPS) through training images for characterizing the spatial structural properties of a surface object/class. Consequently, MPS results are used to increase the accuracy of the fraction image of the surface object/class. The improved fraction image then inputs to the SPM method for producing the land cover map with finer spatial resolution. In order to validate the proposed method, a remotely sensed image from Landsat TM 30 m over the Qianyanzhou red earth hill region in China is used. This experimental study not only compares the results from SPM with improved fraction images with MPS and results from SPM with original fraction images, but also investigates the performances of different soft classifiers. It has been demonstrated that this proposed method is an effective way to reduce the uncertainty in outputs of different soft classification, increase the recognition accuracies of boundary regions and thus increase the accuracies of SPM simulated images. (C) 2012 Elsevier B.V. All rights reserved. |
| 出处 | International Journal of Applied Earth Observation and Geoinformation |
| 卷 | 22 |
| 页 | 115-126 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 0303-2434 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/30538]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Ge Y.. Sub-pixel land-cover mapping with improved fraction images upon multiple-point simulation. 2013. |
入库方式： OAI收割
来源：地理科学与资源研究所
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