Journal of Geo-information Science ›› 2019, Vol. 21 ›› Issue (4): 615-622.doi: 10.12082/dqxxkx.2018.0452

• Orginal Article • Previous Articles     Next Articles

Progressive Morphological Filtering Method of Dense Matching Point Cloud based on Region Feature Segmentation

Gang ZHANG1,2,*(), Wenbin LIU2, Nan ZHANG2   

  1. 1. Chinese Academy of Surveying and Mapping, Beijing 100830, China
    2. Beijing Geo-Vision Technology Company Limited, Beijing 100039, China
  • Received:2018-09-10 Revised:2019-03-25 Online:2019-04-24 Published:2019-04-24
  • Contact: Gang ZHANG E-mail:43131904@qq.com
  • Supported by:
    National Key Research and Development Program of China, No.2017YFB0503004;National S&T Major Project for High-resolution Earth Observation System, No.42-Y2-0A14-9001-17/18

Abstract:

With the progress of computer vision and RS, dense matching based on remote sensing images has also become one of the important means to obtain high-precision point clouds. Like point clouds of LiDAR, filtering is the fundamental step. Dense matching point cloud is similar with LiDAR point cloud, but have different feature. In this paper, the feature condition is added to the progressive morphological filtering algorithm, point clouds and images are combined into RGB-Depth images, and depth images are semantically segmented according to typical object types, so that point clouds which coordinate correspond with image coordinate are marked and filtered for the first time. Then divide point clouds by grid, then do simply classified according to geometric features, and the improved irregular triangular network of ground points is constructed by filter parameters corresponding to the classification results. Finally, use and intergraded the pre-filtering results and the semantic segmentation results, the regions with similar features are optimized and confirmed by predefined parameter, and the final filtering results are obtained. The results are compared with results of the Cloth Simulated Filtering algorithm. The test result was show that type I error less than 1.98%, type II error less than 2.33% of the progressive morphological filtering algorithm, that algorithm is suitable for higher precision application, especially mixed terrain points cloud filtering.

Key words: dense matching, point cloud filtering, cloth simulation, deep learning, region feature segmentation, morphological filtering, UAV