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| an improved spectral similarity measure based on kernel mapping for classification of remotely sensed image | |
| Xia Liegang; Wang Weihong; Hu Xiaodong; Luo Jiancheng | |
| 2012 | |
| Source | Cehui Xuebao/Acta Geodaetica et Cartographica Sinica
 |
| ISSN | 1001-1595 |
| Volume | 41Issue:4Pages:591-596,604 |
| English Abstract | Based on the characteristic of multispectral data, a new function called KSSV is designed in modifying the Gaussian kernel mapping by SSV matching technology. With this function, the feature space of multispectral images could be mapped to high dimension space. Then in the high dimension space, the old similarity measure based on Euclidean distance was replaced by SAM method. In this way, the characteristic information in multispectral images can be exploited adequately and used in many remote sensing applications effectively. At last, the method is applied to unsupervised (k-means clustering) and supervised (minimum distance, SVM) classification experiments. The results show that the classification method with KSSV measure can significantly increase the accuracy of distinguishing between different land types and reduce inconsistency in one category. So the improved method can be more effective in the classification of multi-spectral remote sensing images and achieve better accuracy.; Based on the characteristic of multispectral data, a new function called KSSV is designed in modifying the Gaussian kernel mapping by SSV matching technology. With this function, the feature space of multispectral images could be mapped to high dimension space. Then in the high dimension space, the old similarity measure based on Euclidean distance was replaced by SAM method. In this way, the characteristic information in multispectral images can be exploited adequately and used in many remote sensing applications effectively. At last, the method is applied to unsupervised (k-means clustering) and supervised (minimum distance, SVM) classification experiments. The results show that the classification method with KSSV measure can significantly increase the accuracy of distinguishing between different land types and reduce inconsistency in one category. So the improved method can be more effective in the classification of multi-spectral remote sensing images and achieve better accuracy. |
| Indexed Type | EI |
| Keyword | Image Reconstruction Remote Sensing Support Vector Machines |
| Department | (1) Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China; (2) Software College Zhejiang University of Technology Hangzhou 310023 China; (3) Graduated University Chinese Academy of Sciences Beijing 100049 China |
| Language | 中文 |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15434 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Xia Liegang,Wang Weihong,Hu Xiaodong,et al. an improved spectral similarity measure based on kernel mapping for classification of remotely sensed image[J]. Cehui Xuebao/Acta Geodaetica et Cartographica Sinica,2012,41(4):591-596,604. |
| APA | Xia Liegang,Wang Weihong,Hu Xiaodong,&Luo Jiancheng.(2012).an improved spectral similarity measure based on kernel mapping for classification of remotely sensed image.Cehui Xuebao/Acta Geodaetica et Cartographica Sinica,41(4),591-596,604. |
| MLA | Xia Liegang,et al."an improved spectral similarity measure based on kernel mapping for classification of remotely sensed image".Cehui Xuebao/Acta Geodaetica et Cartographica Sinica 41.4(2012):591-596,604. |
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