Name
Abstract | ||
|---|---|---|
In this paper, we present a domain adaptation network to deal with classification scenarios subjected to the data shift problem (i.e., labeled and unlabeled images acquired with different sensors and over completely different geographical areas). We rely on the power of pretrained convolutional neural networks (CNNs) to generate an initial feature representation of the labeled and unlabeled images... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TGRS.2017.2692281 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Remote sensing,Feature extraction,Neural networks,Feeds,Earth,Machine learning,Computer architecture | Data mining,Data set,Source data,Computer science,Convolutional neural network,Remote sensing,Regularization (mathematics),Artificial intelligence,Contextual image classification,Artificial neural network,Computer vision,Pattern recognition,Feature extraction,Smith–Waterman algorithm | Journal |
Volume | Issue | ISSN |
55 | 8 | 0196-2892 |
Citations | PageRank | References |
3 | 0.37 | 38 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Essam Othman | 1 | 7 | 1.43 |
| Yakoub Bazi | 2 | 672 | 43.66 |
| Farid Melgani | 3 | 1100 | 80.98 |
| Haikel Salem Alhichri | 4 | 147 | 9.72 |
| Naif Alajlan | 5 | 839 | 50.51 |
| Mansour A. Al Zuair | 6 | 41 | 7.79 |