Land Cover Classification of Hyperspectral Data Using Composite Kernel Support Vector Machines
详细信息
- 责任者：Shang Kun (Institute of Remote Sensing Applications ; Chinese Academy of Sciences ; Beijing 100049 ; China) ; Li Peijun
- 刊名：Acta Scientiarum Naturalium Universitatis Pekinensis
- 出版年：2011
- 卷期：47(1)
- 关键词：遥感 ; remote sensing ; 土地覆被 ; land cover ; 支持向量机 ; support vector machine
- 页码：p.109-114
- 图表：3 illus., 3 tables, 14 refs.
- 分类号：1600
- issnNo：ISSN0479-8023
- isbnNo：CN11-2442/N
摘要
Land cover classification using recently developed composite kernel support vector machines (SVM) and hyperspectral data is proposed. The hyperspectral data are first subdivided into different subsets. SVM method is then used to select optimal parameters for classification of each subset. Finally, different subsets are combined by a composite kernel function, and the best one selected from different parameter combinations is used in final land cover classification using composite kernel SVM. The HYDICE data of Washington DC is used to evaluate and validate the proposed method. The results show that land cover classification of hyperspectral data using composite kernel SVM can obtain higher classification accuracy than the traditional SVM method.