Institutional Repository
| A novel multi-scale relative salience feature for remote sensing image analysis | |
| Huang, Zhijian (1); Zhang, Jinfang (2); Xu, Fanjiang (2); Huang, Z.(zhijian07@iscas.ac.cn) | |
| 2014 | |
| Source | Optik
 |
| ISSN | 304026 |
| Volume | 125Issue:1Pages:516-520 |
| English Abstract | This paper presents a novel feature for remote sensing image analysis, called multi-scale relative salience (MsRS) feature. It is constructed by modeling the process of feature value changing with scales. Firstly, the multi-scale observation values at each site are obtained by convolved with recursive Gaussian filters for efficiency. Secondly, the multi-scale observation values are compared with the initial value to generate the relative salience. Lastly, the relative salience between multi-scales are embed into a single feature called the MsRS. The scale in MsRS has explicit spatial meaning which is convenient to choose appropriate scale for specified object. In the MsRS map, the inner of each object become more consistent, while the contrast between object and background is enlarged. The MsRS can be used as preprocessing step of many applications, such as segmentation. Two state-of-art segmentations (the mean shift and the statistical region merging) are taken into experiments and the results proved that it brings improvement obviously. © 2013 Elsevier GmbH.; This paper presents a novel feature for remote sensing image analysis, called multi-scale relative salience (MsRS) feature. It is constructed by modeling the process of feature value changing with scales. Firstly, the multi-scale observation values at each site are obtained by convolved with recursive Gaussian filters for efficiency. Secondly, the multi-scale observation values are compared with the initial value to generate the relative salience. Lastly, the relative salience between multi-scales are embed into a single feature called the MsRS. The scale in MsRS has explicit spatial meaning which is convenient to choose appropriate scale for specified object. In the MsRS map, the inner of each object become more consistent, while the contrast between object and background is enlarged. The MsRS can be used as preprocessing step of many applications, such as segmentation. Two state-of-art segmentations (the mean shift and the statistical region merging) are taken into experiments and the results proved that it brings improvement obviously. © 2013 Elsevier GmbH. |
| Indexed Type | SCI ; EI |
| Keyword | Multi-scale Feature Mean Shift Statistical Region Merging Sift Scale Space |
| Department | (1) SPDF, School of Electronic Science and Engineering, National University of Defense Technology, China; (2) IIST Key Lab., Institute of Software, Chinese Academy of Sciences, Zhongguancun, Beijing 100190, China |
| Language | 英语 |
| WOS ID | WOS:000329537300113 |
| Citation statistics | |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16887 |
| Collection | 中国科学院软件研究所 |
| Corresponding Author | Huang, Z.(zhijian07@iscas.ac.cn) |
| Recommended Citation GB/T 7714 | Huang, Zhijian ,Zhang, Jinfang ,Xu, Fanjiang ,et al. A novel multi-scale relative salience feature for remote sensing image analysis[J]. Optik,2014,125(1):516-520. |
| APA | Huang, Zhijian ,Zhang, Jinfang ,Xu, Fanjiang ,&Huang, Z..(2014).A novel multi-scale relative salience feature for remote sensing image analysis.Optik,125(1),516-520. |
| MLA | Huang, Zhijian ,et al."A novel multi-scale relative salience feature for remote sensing image analysis".Optik 125.1(2014):516-520. |
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