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| 图像分类中基于核映射的光谱匹配度量方法 | |
| 引用本文： | 夏列钢,王卫红,胡晓东,骆剑承.图像分类中基于核映射的光谱匹配度量方法[J].测绘学报,2012,41(4):591-596,604. |
| 作者姓名： | 夏列钢 王卫红 胡晓东 骆剑承 |
| 作者单位： | 1. 中国科学院遥感应用研究所,北京100101／中国科学院研究生院,北京100049 2. 浙江工业大学计算机科学与技术学院／软件学院,浙江杭州,310023 3. 中国科学院遥感应用研究所,北京,100101 |
| 基金项目： | 国家自然科学基金，浙江省自然科学基金杰出青年团队项目 |
| 摘 要： | 针对多光谱遥感数据特点利用SSV匹配技术改进高斯核函数得到新的KSSV函数,然后在由KSSV核函数映射得到的高维空间中利用SAM匹配技术代替基于欧氏距离的相似性度量。如此可以充分挖掘多光谱影像中的波谱特征信息并有效利用,提高模式识别方法应用的有效性。将此方法分别应用于非监督分类(k均值)与监督分类(最小距离、SVM)的试验表明,改进度量的分类方法可显著提高地类间的可区分度并有效降低类内的不一致性,更有效针对多光谱遥感影像中的地物类型,获得较好的精度改进。 |
| 关 键 词： | 相似性度量 光谱匹配 核映射 k均值聚类 支撑向量机 |
An Improved Spectral Similarity Measure Based on Kernel Mapping for Classification of Remotely Sensed Image |
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| XIA Liegang,WANG Weihong,HU Xiaodong,LUO Jiancheng.An Improved Spectral Similarity Measure Based on Kernel Mapping for Classification of Remotely Sensed Image[J].Acta Geodaetica et Cartographica Sinica,2012,41(4):591-596,604. | |
| Authors: | XIA Liegang WANG Weihong HU Xiaodong LUO Jiancheng |
| Institution: | 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 |
| 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. |
| Keywords: | similarity metric spectral matching kernel mapping k-means cluster support vector machines |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |