Using synthetic variable ratio method to fuse multi-source remotely sensed images based on sensor spectral response
文献类型：会议论文
| 作者 | Chen ; Sheng1 ; Luo ; Jiancheng1 ; Shen ; Zhanfeng1 ; Geping2 ; Zhu ; Changrning2 |
| 出版日期 | 2008 |
| 会议名称 | 2008 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) |
| 会议日期 | 2008 |
| 会议地点 | Boston, MA, United states |
| 关键词 | CIELAB color - Multi-spectral - Multiple regression analysis - Multisource - Physical meanings - Quickbird - Remotely sensed images - Spectral characters - Spectral response - Variable ratio |
| 页码 | II1084-II1087 |
| 中文摘要 | Synthetic variable ratio (SVR) method was first introduced to fuse panchromatic (PAN) image and multispectral (MS) image by Muechika et al in 1993[1], and was improved by Zhang Y. in 1999 and 2001 respectively [2, 3]. As for Muechika SVR method, it isn't suitable for fusing PAN and MS which cover large area. And for Zhang Y. SVR(ZY-SVR) method, on the one hand it needs to select a great number of pixels of different land coverage classes to conduct multiple regression analysis and thus it seems greatly empirical; on the other hand the coefficients obtained through regression lack physical meanings. This paper puts forward a new method called LAB-SRV which introduces the sensor spectral response into SVR method using the CIELab color space. Several experiments done in this paper indicate that this method can sharpen multi-spectral MS images without changing their spectral characters very much and have an advantage over ZY-SVR method with PAN and MS of IKONOS and QuickBird. © 2008 IEEE. (4 refs.) |
| 收录类别 | EI |
| 会议录 | 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
 |
| ISBN号 | 13: 9781424428083 |
| 源URL | [http://ir.xjlas.org/handle/365004/10788]  |
| 专题 | 新疆生态与地理研究所_中国科学院新疆生态与地理研究所(2010年以前数据) |
| 推荐引用方式 GB/T 7714 | Chen,Sheng1,Luo,et al. Using synthetic variable ratio method to fuse multi-source remotely sensed images based on sensor spectral response[C]. 见:2008 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Boston, MA, United states. 2008. |
入库方式： OAI收割
来源：新疆生态与地理研究所
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