Abstract
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings |
| Subtitle of host publication | Observing, Understanding And Forecasting The Dynamics Of Our Planet |
| Publisher | IEEE Xplore |
| Pages | 2099-2102 |
| ISBN (Electronic) | 9781538671504, 9781538671498 |
| ISBN (Print) | 9781538671511 |
| DOIs | |
| Publication status | Published - 5 Nov 2018 |
| Event | IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018 |
Conference
| Conference | IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium |
|---|---|
| Country | Spain |
| City | Valencia |
| Period | 22/07/18 → 27/07/18 |
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Improving Maps from CNNs Trained with Sparse, Scribbled Ground Truths Using Fully Connected CRFs. / Maggiolo, Luca; Marcos, Diego; Moser, Gabriele; Tuia, Devis.
2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings: Observing, Understanding And Forecasting The Dynamics Of Our Planet. IEEE Xplore, 2018. p. 2099-2102.Research output: Chapter in Book/Report/Conference proceeding › Conference paper
TY - GEN
T1 - Improving Maps from CNNs Trained with Sparse, Scribbled Ground Truths Using Fully Connected CRFs
AU - Maggiolo, Luca
AU - Marcos, Diego
AU - Moser, Gabriele
AU - Tuia, Devis
PY - 2018/11/5
Y1 - 2018/11/5
N2 - Convolutional Neural Networks (CNNs) have become the new standard for semantic segmentation of very high resolution images. But as for other methods, the map accuracy depends on the quantity and quality of ground truth used to train them. Having densely annotated data, i.e. a detailed, pixel-level ground truth (GT), allows obtaining effective models, but requires high efforts in annotation. For this reason, it is more common and efficient to work with point or scribbled annotations rather than with dense ones. A CNN model trained with such incomplete ground truths tends to mischaracterize the shapes of the objects and to be inaccurate near their boundaries. We propose to use an approximation of a fully connected Conditional Random Field (CRF) to solve these issues, in which long range connections are accounted for through auxiliary nodes based on clustering of CNN activation features. Experiments on the ISPRS Vaihingen benchmark, where a CNN is trained only with a non-dense, scribbled ground truth, show that the proposed method can fill part of the performance gap with respect to models trained on the densely annotated, but unrealistic, ground truth.
AB - Convolutional Neural Networks (CNNs) have become the new standard for semantic segmentation of very high resolution images. But as for other methods, the map accuracy depends on the quantity and quality of ground truth used to train them. Having densely annotated data, i.e. a detailed, pixel-level ground truth (GT), allows obtaining effective models, but requires high efforts in annotation. For this reason, it is more common and efficient to work with point or scribbled annotations rather than with dense ones. A CNN model trained with such incomplete ground truths tends to mischaracterize the shapes of the objects and to be inaccurate near their boundaries. We propose to use an approximation of a fully connected Conditional Random Field (CRF) to solve these issues, in which long range connections are accounted for through auxiliary nodes based on clustering of CNN activation features. Experiments on the ISPRS Vaihingen benchmark, where a CNN is trained only with a non-dense, scribbled ground truth, show that the proposed method can fill part of the performance gap with respect to models trained on the densely annotated, but unrealistic, ground truth.
U2 - 10.1109/IGARSS.2018.8517947
DO - 10.1109/IGARSS.2018.8517947
M3 - Conference paper
SN - 9781538671511
SP - 2099
EP - 2102
BT - 2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings
PB - IEEE Xplore
ER -