Super-Resolution Land Cover Mapping Based on Multiscale Spatial Regularization
文献类型：SCI/SSCI论文
| 作者 | Hu J. L.; Ge, Y.; Chen, Y. H.; Li, D. Y. |
| 发表日期 | 2015 |
| 关键词 | Fraction images heterogeneity homogeneity multiscale regularization remote sensing spatial dependence super-resolution mapping (SRM) markov-random-field remotely-sensed images hopfield neural-network model identification dependence algorithm map |
| 英文摘要 | Super-resolution mapping (SRM) is a method for allocating land cover classes at a fine scale according to coarse fraction images. Based on a spatial regularization framework, this paper proposes a new regularization method for SRM that integrates multiscale spatial information from the fine scale as a smooth term and from the coarse scale as a penalty term. The smooth term is considered a homogeneity constraint, and the penalty term is used to characterize the heterogeneity constraint. Specifically, the smooth term depends on the local fine scale spatial consistency, and is used to smooth edges and eliminate speckle points. The penalty term depends on the coarse scale local spatial differences, and suppresses the over-smoothing effect from the fine scale information while preserving more details (e.g., connectivity and aggregation of linear land cover patterns). We validated our method using simulated and synthetic images, and compared the results to four representative SRM algorithms. Our numerical experiments demonstrated that the proposed method can produce more accurate maps, reduce differences in the number of patches, visually preserve smoother edges and more details, reject speckle points, and suppress over-smoothing. |
| 出处 | Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| 卷 | 8 |
| 期 | 5 |
| 页 | 2031-2039 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 1939-1404 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/38443]  |
| 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 | Hu J. L.,Ge, Y.,Chen, Y. H.,et al. Super-Resolution Land Cover Mapping Based on Multiscale Spatial Regularization. 2015. |
入库方式： OAI收割
来源：地理科学与资源研究所
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