Abstract
Significantly outperforming traditional machine learning methods, deep convolutional neural networks have gained increasing popularity in the application of image classification and segmentation. Nevertheless, deep learning-based methods usually require a large amount of training data, which is quite labor-intensive and time-demanding. To deal with the problem in generating training data, we propose in this paper a novel approach to generate image annotations by transferring labels from aerial images to UAV images and refine the annotations using a densely connected CRF model with an embedded naive Bayes classifier. The generated annotations not only present correct semantic labels, but also preserve accurate class boundaries. To validate the utility of these automatic annotations, we deploy them as training data for pixel-wise image segmentation and compare the results with the segmentation using manual annotations. Experiment results demonstrate that the automatic annotations can achieve comparable segmentation accuracy as the manual annotations.
Originalsprache | englisch |
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Titel | 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers |
Seiten | 3461-3464 |
Seitenumfang | 4 |
ISBN (elektronisch) | 9781538671504 |
DOIs | |
Publikationsstatus | Veröffentlicht - 31 Okt. 2018 |
Veranstaltung | 38th Annual IEEE International Geoscience and Remote Sensing Symposium: IGARSS 2018 - Valencia, Valencia, Spanien Dauer: 22 Juli 2018 → 27 Juli 2018 https://igarss2018.org |
Publikationsreihe
Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
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Band | 2018-July |
Konferenz
Konferenz | 38th Annual IEEE International Geoscience and Remote Sensing Symposium |
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Kurztitel | IGARSS |
Land/Gebiet | Spanien |
Ort | Valencia |
Zeitraum | 22/07/18 → 27/07/18 |
Internetadresse |
ASJC Scopus subject areas
- Angewandte Informatik
- Erdkunde und Planetologie (insg.)