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机译:评估新分类器-GP-OAD的性能:与现有方法的比较,根据高光谱图像对岩石类型和矿物学进行分类
Australian Centre for Field Robotics, The Rose Street Building, J04, Department of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, NSW, Australia;
Australian Centre for Field Robotics, The Rose Street Building, J04, Department of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, NSW, Australia;
Australian Centre for Field Robotics, The Rose Street Building, J04, Department of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, NSW, Australia;
Hyperspectral; Absorption feature; Iron minerals; Vertical geology; Illumination conditions; Machine learning; Classification; Remote sensing;
机译:使用高光谱影像对树种进行分类:两个分类器的比较
机译:使用星载角高光谱(CHRIS / Proba)影像评估系综分类器以绘制比利时Natura 2000荒地的地图
机译:使用多分类器系统评估降维方法对信息类和分类器的影响,以进行高光谱图像分类
机译:随机森林和最大似然分类器与WORLDVIEW-2影像对作物类型分类的比较
机译:利用稀疏性和字典学习来有效地分类高光谱图像中的材料。
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:评估新分类器GP-OAD的性能:与现有方法进行比较,以根据高光谱图像对岩石类型和矿物学进行分类