Lidar Cloud Detection with Fully Convolutional Networks
| Autor: | Donna Flynn, Erol Cromwell |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning 010504 meteorology & atmospheric sciences Backscatter business.industry Computer science Supervised learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Cloud computing Pattern recognition Machine Learning (stat.ML) Image segmentation Atmospheric model 01 natural sciences Data modeling Machine Learning (cs.LG) Identification (information) Lidar Statistics - Machine Learning 0103 physical sciences Artificial intelligence business 010303 astronomy & astrophysics 0105 earth and related environmental sciences |
| Zdroj: | WACV |
| DOI: | 10.48550/arxiv.1805.00928 |
| Popis: | 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. Comment: Updated for full version of paper. 10 pages, submitted to NIPS 2018 Conference (in review) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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