Abstract
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.
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 |