Name
Abstract | ||
|---|---|---|
Deep learning has been applied to segment buildings from high-resolution images with promising results. However, there still exist the problems stemming from training on split patches and class imbalances. To overcome these problems, we propose a dual-resolution U-Net that uses pairs of images as inputs to capture both high and low resolution features. We also employ a soft Jaccard loss to place more emphasis on the sparse and low accuracy samples. The images from different regions are further balanced according to their building densities. With our architecture, we achieved state-of-the-art results on the Inria aerial image labeling dataset without any post-processing. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICPR.2018.8545190 | 2018 24th International Conference on Pattern Recognition (ICPR) |
Keywords | Field | DocType |
Inria aerial image labeling dataset,dual-resolution U-Net,building extraction,aerial images,deep learning,soft Jaccard loss,building segmentation | Computer vision,Architecture,Task analysis,Pattern recognition,Computer science,Feature extraction,Image segmentation,Aerial image,Jaccard index,Artificial intelligence,Deep learning,Image resolution | Conference |
ISSN | ISBN | Citations |
1051-4651 | 978-1-5386-3789-0 | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kangkang Lu | 1 | 0 | 0.34 |
| Ying Sun | 2 | 291 | 40.03 |
| SH Ong | 3 | 77 | 10.60 |