Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2017-2022, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2017-2018
© 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, 2017-2022, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2017-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD

Z. Xu1 and Z. Yang2 Z. Xu and Z. Yang
  • 1Airborne patrolling center of Guangdong power grid Co.,Ltd
  • 2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing of Wuhan University, Wuhan 430079, China

Keywords: EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD

Abstract. The classification of point clouds is the first step in the extraction of various types of geo-information form point clouds. Recently the ISPRS WG II/4 provides a benchmark on 3D semantic labelling, a convolutional neural network based method achieves the best overall accuracy performance in all participants who only use the geometrical and waveform based features extracted from the ALS data. Features of the point are calculated in different scales to achieve the best performance. It is not efficiency for the future use. In this paper, we use an eigenentropy based scale selection strategy to improve this method. The scale selection strategy improves the average F1 score and makes the classification method more simple and efficient.