Lidar Cloud Detection with Fully Convolutional Networks

Erol Cromwell, Donna Flynn

Abstract: In this contribution, we present a novel approach for segmenting laser radar (lidar) imagery into geometric time-height cloud locations with a fully convolutional net- work (FCN). We describe a semi-supervised learning method to train the FCN by: pre-training the classification layers of the FCN with “weakly labeled” lidar data, using “unsupervised” pre-training with the cloud locations of the Wang & Sassen (2001) 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

Keywords: neural network, image segmentation, cloud detection, semi-supervised

TL;DR: We train a fully convolutional network to segment clouds from lidar imagery using a semi-supervised learning approach