The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 79–86, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-79-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 79–86, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-79-2019

  04 Jun 2019

04 Jun 2019

MULTISPECTRAL AIRBORNE LASER SCANNING POINT-CLOUDS FOR LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS

L. Ma1, Z. Chen1, Y. Li1, D. Zhang1, J. Li1,2,3, and M. A. Chapman4 L. Ma et al.
  • 1Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada
  • 2Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
  • 3Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Information Science and Engineering, Xiamen University, Xiamen, Fujian 361005, China
  • 4Department of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada

Keywords: land cover classification, multispectral airborne laser scanning, point cloud, CNN, deep learning

Abstract. This paper presents an automated workflow for pixel-wise land cover (LC) classification from multispectral airborne laser scanning (ALS) data using deep learning methods. It mainly contains three procedures: data pre-processing, land cover classification, and accuracy assessment. First, a total of nine raster images with different information were generated from the pre-processed point clouds. These images were assembled into six input data combinations. Meanwhile, the labelled dataset was created using the orthophotos as the ground truth. Also, three deep learning networks were established. Then, each input data combination was used to train and validate each network, which developed eighteen LC classification models with different parameters to predict LC types for pixels. Finally, accuracy assessments and comparisons were done for the eighteen classification results to determine an optimal scheme. The proposed method was tested on six input datasets with three deep learning classification networks (i.e., 1D CNN, 2D CNN, and 3D CNN). The highest overall classification accuracy of 97.2% has been achieved using the proposed 3D CNN. The overall accuracy (OA) of the 2D and 3D CNNs was, on average, 8.4% higher than that of the 1D CNN. Although the OA of the 2D CNN was at most 0.3% lower than that of the 3D CNN, the runtime of the 3D CNN was five times longer than the 2D CNN. Thus, the 2D CNN was the best choice for the multispectral ALS LC classification when considering efficiency. The results demonstrated the proposed methods can successfully classify land covers from multispectral ALS data.