Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1883-1887, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1883-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1883-1887, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1883-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

3D SEMANTIC LABELING OF ALS DATA BASED ON DOMAIN ADAPTION BY TRANSFERRING AND FUSING RANDOM FOREST MODELS

J. Wu1,2, W. Yao2, J. Zhang3, and Y. Li4 J. Wu et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
  • 2Dept. of Land Surveying & Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong
  • 3State Grid Economic and Technological Research Institute CO.LTD, Beijing, China
  • 4Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan, China

Keywords: 3D semantic labelling, ALS data, random forest, domain adaption, decision fusion

Abstract. Labeling 3D point cloud data with traditional supervised learning methods requires considerable labelled samples, the collection of which is cost and time expensive. This work focuses on adopting domain adaption concept to transfer existing trained random forest classifiers (based on source domain) to new data scenes (target domain), which aims at reducing the dependence of accurate 3D semantic labeling in point clouds on training samples from the new data scene. Firstly, two random forest classifiers were firstly trained with existing samples previously collected for other data. They were different from each other by using two different decision tree construction algorithms: C4.5 with information gain ratio and CART with Gini index. Secondly, four random forest classifiers adapted to the target domain are derived through transferring each tree in the source random forest models with two types of operations: structure expansion and reduction-SER and structure transfer-STRUT. Finally, points in target domain are labelled by fusing the four newly derived random forest classifiers using weights of evidence based fusion model. To validate our method, experimental analysis was conducted using 3 datasets: one is used as the source domain data (Vaihingen data for 3D Semantic Labelling); another two are used as the target domain data from two cities in China (Jinmen city and Dunhuang city). Overall accuracies of 85.5 % and 83.3 % for 3D labelling were achieved for Jinmen city and Dunhuang city data respectively, with only 1/3 newly labelled samples compared to the cases without domain adaption.