Department of Computer Science, Xiamen University, Xiamen, China;
Department of Computer Science, Xiamen University, Xiamen, China;
School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China;
School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China;
Department of Computer Science, Xiamen University, Xiamen, China;
Department of Geography and Environmental Management, University of Waterloo, Canada N2L 3G1;
Domain adapation; remote sensing; hyperspectral image classification; support vector machines; maximum mean discrepancy;
机译:基于SVM的实时高光谱图像分类器在多核体系结构上
机译:基于光谱空间SVM的多层学习算法用于高光谱图像分类
机译:使用内在维的基于SVM的高光谱图像分类
机译:使用基于SVM的域自适应分类器进行高光谱图像分类
机译:多分类器和决策融合,可用于高光谱分类的稳健统计模式识别。
机译:基于模糊性的主动学习框架可增强区分性和生成性分类器的高光谱图像分类性能
机译:为基于SVM的高光谱图像分类定制内核功能