Volume XLI-B1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 741-747, 2016
https://doi.org/10.5194/isprs-archives-XLI-B1-741-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 741-747, 2016
https://doi.org/10.5194/isprs-archives-XLI-B1-741-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  06 Jun 2016

06 Jun 2016

3D LAND COVER CLASSIFICATION BASED ON MULTISPECTRAL LIDAR POINT CLOUDS

Xiaoliang Zou1,2,3, Guihua Zhao3, Jonathan Li2, Yuanxi Yang1,3, and Yong Fang1,3 Xiaoliang Zou et al.
  • 1State Key Laboratory of Geo-Information Engineering, Xi’an, China 710054
  • 2Department of Geography and Environmental Management, Faculty of Environment, University of Waterloo, Waterloo, ON, Canada N2L 3G1
  • 3Xi’an Institute of Surveying and Mapping, Xi’an, China 710054

Keywords: Multispectral Lidar, OBIA, Intensity Imagery, Multi-resolution Segmentation, Classification, Accuracy Assessment, 3D Land Cover Classification

Abstract. Multispectral Lidar System can emit simultaneous laser pulses at the different wavelengths. The reflected multispectral energy is captured through a receiver of the sensor, and the return signal together with the position and orientation information of sensor is recorded. These recorded data are solved with GNSS/IMU data for further post-processing, forming high density multispectral 3D point clouds. As the first commercial multispectral airborne Lidar sensor, Optech Titan system is capable of collecting point clouds data from all three channels at 532nm visible (Green), at 1064 nm near infrared (NIR) and at 1550nm intermediate infrared (IR). It has become a new source of data for 3D land cover classification. The paper presents an Object Based Image Analysis (OBIA) approach to only use multispectral Lidar point clouds datasets for 3D land cover classification. The approach consists of three steps. Firstly, multispectral intensity images are segmented into image objects on the basis of multi-resolution segmentation integrating different scale parameters. Secondly, intensity objects are classified into nine categories by using the customized features of classification indexes and a combination the multispectral reflectance with the vertical distribution of object features. Finally, accuracy assessment is conducted via comparing random reference samples points from google imagery tiles with the classification results. The classification results show higher overall accuracy for most of the land cover types. Over 90% of overall accuracy is achieved via using multispectral Lidar point clouds for 3D land cover classification.