Name
Abstract | ||
|---|---|---|
Land cover classification is a flourishing research topic in the field of remote sensing. Conventional methodologies mainly focus either on the simplified single-label case or on the pixel-based approaches that cannot efficiently handle high-resolution images. On the other hand, the problem of multilabel land cover scene categorization remains, to this day, fairly unexplored. While deep learning a... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LGRS.2019.2893306 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Training,Feature extraction,Deep learning,Remote sensing,Data models,Convolutional neural networks,Sensors | Data modeling,Categorization,Computer vision,Convolutional neural network,Feature extraction,Artificial intelligence,Pixel,Deep learning,Contextual image classification,Land cover,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
16 | 7 | 1545-598X |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Radamanthys Stivaktakis | 1 | 2 | 0.36 |
| Grigorios Tsagkatakis | 2 | 122 | 21.53 |
| P. Tsakalides | 3 | 954 | 120.69 |