图像分类中基于核映射的光谱匹配度量方法

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。 更多还原%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