The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 247–254, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-247-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 247–254, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-247-2020

  12 Aug 2020

12 Aug 2020

SIMULATION-BASED DATA AUGMENTATION USING PHYSICAL PRIORS FOR NOISE FILTERING DEEP NEURAL NETWORK

M. Jameela1, L. Chen2, A. Sit2, J. Yoo1, C. Verheggen2, and G. Sohn1 M. Jameela et al.
  • 1Department of Earth and Space Science and Engineering, Lassonde School of Engineering York University, Canada
  • 2Teledyne Optech, Canada

Keywords: Mobile Mapping System, Airborne LiDAR, Remote Sensing, 3D Representation, Noise Filtering, Systematic Noise, Data Augmentation

Abstract. LiDAR (Light Detection and Ranging) mounted with static and mobile vehicles has been rapidly adopted as a primary sensor for mapping natural and built environments for a range of civil and military applications. Recently, technology advancement in electro-optical engineering enables acquiring laser returns at high pulse repetition frequency (PRF) from 100Hz to 2MHz for airborne LiDAR, which leads to an increase in the density of 3D point cloud significantly. Traditional systems with lower PRF had a single pulse-in-air zone (PIA) big enough to avoid a mismatch between pulse pair at the receiver. Modern multiple pulses-in-air (MPIA) technology ensures multiple windows of operational ranges for single flight line and no blind-zones; downside of the technology is projection of atmospheric returns closer to same PIA zone of neighbouring ground points and more likely to be overlapping with objects of interest. These characteristics of noise compromise the quality of the scene and encourage usage of noise filtering neural network as existing filters are not effective. A noise filtering deep neural network requires a considerable volume of the diverse annotated dataset, which is expensive. We developed simulation for data augmentation based on physical priors and Gaussian generative function. Our study compares deep learning networks for noise filtering and shows performance gain on 3D U-Net. Then, we evaluate 3D U-Net for simulation-based data augmentation, which shows an increase in precision and F1-score. We also provide an analysis of the underline spatial distribution of points and their impact on data augmentation, and noise filtering.