Hyperspectral Computing Laboratory (Hypercomp), Department of Technology of Computers and Communications, Escuela Politecnica, University of Extremadura, Caceres, E-10071, Spain;
School of Geography and Planning and Guangdong Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-sen University, Guangzhou, P. R. China;
Hyperspectral Computing Laboratory (Hypercomp), Department of Technology of Computers and Communications, Escuela Politecnica, University of Extremadura, Caceres, E-10071, Spain;
Hyperspectral Computing Laboratory (Hypercomp), Department of Technology of Computers and Communications, Escuela Politecnica, University of Extremadura, Caceres, E-10071, Spain,State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;
Hyperspectral remote sensing; image classification; spectral partitioning; multiple band selecting criteria; high-performance computing;
机译:面向类的光谱分割用于遥感高光谱图像分类
机译:使用多GPU计算智能分类大型远程感测的高光谱图像
机译:主动度量学习用于遥感高光谱图像分类
机译:一种用于远程感测超细图像分类的多标准基于标准的光谱分区方法
机译:超类:一种用于高光谱遥感数据的无监督分类方法。
机译:利用自组织图上的遥感高光谱数据对棉花轮虫的数量进行分类
机译:区域对象聚合远程感测到远程斑点图像的图像分割方法研究