an improved spectral similarity measure based on kernel mapping for classification of remotely sensed image
文献类型：期刊论文
| 作者 | Xia Liegang ; Wang Weihong ; Hu Xiaodong ; Luo Jiancheng |
| 刊名 | Cehui Xuebao/Acta Geodaetica et Cartographica Sinica
 |
| 出版日期 | 2012 |
| 卷号 | 41期号:4页码:591-596,604 |
| 关键词 | Image reconstruction Remote sensing Support vector machines |
| ISSN号 | 1001-1595 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 语种 | 中文 |
| 公开日期 | 2013-09-17 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/15434]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：软件研究所
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