Lidar Cloud Detection with Fully Convolutional Networks

Conference · Thu Mar 07 04:00:00 EST 2019

DOI:https://doi.org/10.1109/WACV.2019.00071· OSTI ID:1650662

Cromwell, Erol LD ^[1]; Flynn, Donna M. ^[1]

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1. BATTELLE (PACIFIC NW LAB)

In this contribution, we present a novel approach for segmenting laser radar (lidar) imagery into geometric time- height cloud locations with a fully convolutional network (FCN). We describe a semi-supervised learning method to train the FCN by: pre-training the classification layers of the FCN with image-level annotations, pre-training the en- tire FCN with the cloud locations of the MPLCMASK cloud mask algorithm, and fully supervised learning with hand-labeled cloud locations. We show the model achieves higher levels of cloud identification compared to the cloud mask algorithm implementation.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1650662
• Report Number(s): PNNL-SA-138402
• Country of Publication: United States
• Language: English