With the development of image dense matching method, point clouds can be obtained from multi-view oblique aerial images, whose accuracy and density can be comparable with LiDAR point clouds. However, the currently derived colored point clouds lack classification information. In view of such a situation, this paper proposes an object-based classification method for oblique photogrammetric point clouds. The first step of this method is to calculate features of each point. Then, SLIC algorithm is used to divide the corresponding image into super-pixels. After that, point clouds are clustered into super-voxels as objects according to the relationship between point clouds and images, and features of each object are calculated afterwards. Random forests algorithm is used to classify these super-voxels. Finally, contextual information is adopted to optimize the initial classification results. Two sets of data were employed for evaluating the proposed method, and the overall accuracy could reach up to 91.2% and 88.1% respectively, which improves the precision by 2.3% and 8.2% compared with the point-based classification.
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2018, Vol. 30 
), 邹峥嵘, 张云生, 杜守基, 郑特
