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 entire 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.
@article{arxiv.1805.00928,
title = {Lidar Cloud Detection with Fully Convolutional Networks},
author = {Erol Cromwell and Donna Flynn},
journal= {arXiv preprint arXiv:1805.00928},
year = {2018}
}
Comments
Updated for full version of paper. 10 pages, submitted to NIPS 2018 Conference (in review)