Hyperspectral Image Classification with SVM-based Domain Adaption Classifiers

摘要：

　　A common assumption in hyperspectral image classification is that the distribution of the classes is stable for all the areas of hyperspectral image.However,this assumption is often incorrect due to the inner-class variety over even short distance on the ground.In this paper,we present a semisupervised support vector machine (SVM) framework to learn the cross-domain kernels from both the source and target domain in hyperspectral data.The proposed method simultaneously learns the cross-domain kernel mapping and a robust SVM classifier,which is done by minimizing both the Maximum Mean Discrepancy and structural risk functional of SVM.Experiments are carried out on two real data sets and results show that the proposed model can achieve high classification accuracy and provide robust solutions.

关键词： Domain adapation remote sensing hyperspectral image classification support vector machines maximum mean discrepancy

作者: Zhuo Sun Cheng Wang Peng Li Hanyun Wang Jonathan Li

作者单位: Department of Computer Science, Xiamen University, Xiamen, China School of Electronic Science and Engineering, National University of Defense Technology, Changsha, C Department of Computer Science, Xiamen University, Xiamen, China;Department of Geography and Environ

会议类型: 国际会议

会议名称: 2012年遥感中的计算机视觉国际会议

会议地点: 厦门

会议语种:英文

页码: 1-5

在线出版日期: 2012-12-16（万方平台首次上网日期，不代表论文的发表时间）