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.
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 3461-3464 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538671504 |
| DOIs | |
| Publication status | Published - 31 Oct 2018 |
| Event | 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018 |
Publication series
| Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
|---|---|
| Volume | 2018-July |
Conference
| Conference | 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 |
|---|---|
| Country | Spain |
| City | Valencia |
| Period | 22/07/18 → 27/07/18 |
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Keywords
- Automatic image annotation
- Deep learning
- Image segmentation
- Label propagation
ASJC Scopus subject areas
- Computer Science Applications
- Earth and Planetary Sciences(all)
Cite this
Building detection and segmentation using a CNN with automatically generated training data. / Zhuo, Xiangyu; Fraundorfer, Friedrich; Kurz, Franz; Reinartz, Peter.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers, 2018. p. 3461-3464 8518521 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - Building detection and segmentation using a CNN with automatically generated training data
AU - Zhuo, Xiangyu
AU - Fraundorfer, Friedrich
AU - Kurz, Franz
AU - Reinartz, Peter
PY - 2018/10/31
Y1 - 2018/10/31
N2 - 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.
AB - 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.
KW - Automatic image annotation
KW - Deep learning
KW - Image segmentation
KW - Label propagation
UR - http://www.scopus.com/inward/record.url?scp=85064182092&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2018.8518521
DO - 10.1109/IGARSS.2018.8518521
M3 - Conference contribution
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3461
EP - 3464
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers
ER -