| 支持向量机在SAR图像解译中的研究进展 |
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| 引用本文： | 吴涛,阮祥伟,谭剑波.支持向量机在SAR图像解译中的研究进展[J].遥感信息,2009,0(5):90-95. |
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| 作者姓名： | 吴涛 阮祥伟 谭剑波 |
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| 作者单位： | 中国电子科技集团公司第38研究所,合肥,230031 |
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| 摘 要： | 支持向量机(SVM)是统计学习理论的最新方法，在模式分类和回归分析中有广泛的应用。针对SAR图像解译，SVM在图像滤波、图像分割、目标识别与分类、极化数据分类等过程中有很好的处理能力。本文首先介绍了SVM的基本原理，然后综述SVM在SAR图像解译中的各方面研究进展。
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| 关 键 词： | SAR 图像解译 支持向量机 |
| 收稿时间： | 2008-10-09 |
| 修稿时间： | 2008-11-03 |
Development of Support Vector Machine in SAR Image Interpretation
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| WU Tao,RUAN Xiang-wei,TAN Jian-bo.Development of Support Vector Machine in SAR Image Interpretation
[J].Remote Sensing Information,2009,0(5):90-95. |
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| Authors: | WU Tao RUAN Xiang-wei TAN Jian-bo |
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| Affiliation: | (The 38^th Research Institute of China Electronics Technology Group Corporation, He f ei 230031) |
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| Abstract: | Support Vector Machine (SVM) is the latest method of statistical learning theory, widely used in pattern classification and regression analysis. Aimed at SAR image interpretation, SVM shows good performance in image filtering, image segmentation, target discrimination and classification, as well as polarimetric SAR data classification. The paper first introduces the basic theory of SVM, then reviews the development of SVM in SAR image interpretation. |
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| Keywords: | SAR |
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