机译:一种评估高空间分辨率遥感影像分割质量的新方法
State Key Laboratory of Remote Sensing Science, Research Centre for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing 100875, China,School of Surveying & Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China,Beijing Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China;
State Key Laboratory of Remote Sensing Science, Research Centre for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing 100875, China,Beijing Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China;
State Key Laboratory of Remote Sensing Science, Research Centre for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing 100875, China,Beijing Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China,School of Urban and Environmental Sciences, Huaiyin Normal University, Huaiyin 223300, China;
State Key Laboratory of Remote Sensing Science, Research Centre for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing 100875, China,Beijing Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China;
机译:基于Visual-Lock和Doc2Vec模型的场景分类,用于高空间分辨率遥感图像
机译:使用超高分辨率的遥感影像基于SVM的城市树种软分类
机译:高空间分辨率遥感影像的分层多尺度分割的对象特定优化
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机译:评估非常高分辨率多光谱空中图像中城市植被的无监督分割的替代方法
机译:基于CNN的土地覆盖分类,分层分割和融合点云和非常高空间分辨率遥感图像数据