基于多核Boosting多特征组合高光谱分类技术研究-大地测量学与测量工程专业论文.docxVIP

目录 目录 西安科 西安科技大学全日制工程硕士学位论文 万方数据 万方数据 万方数据 万方数据 阵，再后利用 Boosting 算法对不同给定特征的核矩阵进行学习得到不同权重，最终得到 组合后强 SVM 核矩阵，用 SVM 进行分类，从而实现多特征组合。 关键词：高光谱影像分类；多核 Boosting 学习；空间-光谱特征组合；弱分类器；SVM 研究类型：应用研究 III Subject: Classification of Hyperspectral data based on multi-feature combination by Multiple Kernel Boosting Specialty: Geodesy and Survey Engineering Name : Guo Liankun （Signature） Instructor : Hu Rongming （Signature） ABSTRACT The advantages of hyperspectral remote sensing, which has high spectral resolution, numerous number of bands, and the unity of graphics and spectral, opened the prelude to another revolution. Hyperspectral remote sensing classification technology is a effective geographic information technology of data mining.it is a further development of remote sensing data classification based on traditional technology, which classification technique is unique. When the sensor resolution is too low, a pixel mixed with a variety of surface features which covering mutual intermixing images caused the synonyms spectrum, with spectrum foreign body phenomenon .The presence will cause uncertainty of the hyperspectral image classification, the classification uncertainties faced the biggest challenge in the remote sensing classification. We must understand the nature of these uncertainties, appropriate handling of these uncertainties, and establish a reliable and strong robust hyperspectral image classification to improve multi-class terrain classification accuracy in hyperspectral image classification. And the redundancy numerous bands brought hyperspectral remote sensing image data cause huge image data preprocessing difficult high correlation between the band brings an increase in the number of training samples and hyperspectral remote sensing data classification model using conventional estimation parameters especially suffering problems when statistical classification model; therefore design an appropriate and strong robustness suitable for hyperspectral data classificat