Impact of Training Set Size on the Ability of Deep Neural Networks to Deal with Omission Noise
Deep Learning usually requires large amounts of labeled training data. In remote sensing, deep learning is often applied for land cover and land use classification as well as street network and building segmentation. In case of the latter, a common way of obtaining training labels is to leverage cro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Remote Sensing |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2022.932431/full |