Name
Abstract | ||
|---|---|---|
In this letter, we face the problem of multilabeling unmanned aerial vehicle (UAV) imagery, typically characterized by a high level of information content, by proposing a novel method based on convolutional neural networks. These are exploited as a means to yield a powerful description of the query image, which is analyzed after subdividing it into a grid of tiles. The multilabel classification ta... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LGRS.2017.2671922 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Unmanned aerial vehicles,Training,Neural networks,Feature extraction,Histograms,Image segmentation,Computer architecture | Computer vision,Histogram,Computer science,Convolutional neural network,Image segmentation,Feature extraction,Artificial intelligence,Thresholding,Deep learning,Artificial neural network,Grid | Journal |
Volume | Issue | ISSN |
14 | 5 | 1545-598X |
Citations | PageRank | References |
8 | 0.51 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdallah Zeggada | 1 | 43 | 4.12 |
| Farid Melgani | 2 | 1100 | 80.98 |
| Yakoub Bazi | 3 | 672 | 43.66 |