|
| 论文编号: |
|
| 第一作者所在部门: |
|
| 中文论文题目: |
Simultaneous change region and pattern identification for very high-resolution images
|
| 英文论文题目: |
Simultaneous change region and pattern identification for very high-resolution images
|
| 论文题目英文: |
|
| 作者: |
Huo, Chunlei
|
| 论文出处: |
|
| 刊物名称: |
JOURNAL OF APPLIED REMOTE SENSING
|
| 年: |
2017
|
| 卷: |
11 |
| 期: |
|
| 页: |
|
| 联系作者: |
|
| 收录类别: |
|
| 影响因子: |
|
| 摘要: |
By taking advantages of fine details obtained by the improved spatial resolution, very high-resolution images are promising for detecting change regions and identifying change patterns. However, high overlaps between different change patterns and the complexities of multiclass classification make it difficult to reliably separate change features. A framework named simultaneous change region and pattern identification (SCRAPI) is proposed for simultaneously detecting change regions and identifying change patterns, whose components are aimed at capturing overlaps between change patterns and reducing overlaps driven by user-specific interests. To validate the effectiveness of SCRAPI, a supervised approach is illustrated within this framework, which starts with modeling the relationship between change features by interclass couples and intraclass couples, followed by metric learning where structural sparsity is captured by the mixed norm. Experiments demonstrate the effectiveness of the proposed approach. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE) |
| 英文摘要: |
|
| 外单位作者单位: |
|
| 备注: |
|
关闭窗口 |
|
