机译:基于知识的可转移方法用于基于块的城市土地利用分类
Faculty of Civil, Geo and Environmental Engineering, Technische Universitaet Muenchen, Munich 82234, Germany,Remote Sensing Division, National Institute for Space Research, Sao Jose dos Campos 12227-010, Brazil;
Remote Sensing Division, National Institute for Space Research, Sao Jose dos Campos 12227-010, Brazil;
Electric Engineering Department, Catholic University of Rio de Janeiro, Rio de Janeiro, 38097, Brazil,Department of Computer Engineering, Rio de Janeiro State University, Rua Sao Francisco Xavier, 524, Rio de Janeiro, Brazil;
Electric Engineering Department, Catholic University of Rio de Janeiro, Rio de Janeiro, 38097, Brazil,Department of Computer Engineering, Rio de Janeiro State University, Rua Sao Francisco Xavier, 524, Rio de Janeiro, Brazil;
机译:使用来自高分辨率全色影像的多尺度纹理度量的神经网络方法用于城市土地利用分类
机译:基于块的嘈杂/清洁图像使用常用矢量方法的图像分类
机译:基于轮廓的变换和SVM的基于块的快速纹理图像分割和分类方法
机译:基于知识的多传感器土地利用分类和生物量监测方法
机译:面向对象的城市土地覆盖和土地使用分类方法
机译:使用WorldView-2 / 3和LiDAR数据融合方法和深度学习对城市树种进行分类
机译:城市土地利用分类的景观模式和建筑功能,街区遥感图像 - 以武汉武昌区为例