Name
Abstract | ||
|---|---|---|
In order to describe images acquired with unmanned aerial vehicles (UAV), we introduce in this paper a multilabeling classification method. It starts by subdividing the original UAV image into a grid of tiles which are then analyzed separately. From each tile, a signature which encodes texture information is extracted and compared with the signatures of the tiles belonging to a pre-built training dictionary in order to acquire the binary multilabel vector of the most similar tile. In order to represent and match the tiles, we exploit a well-known texture operator and a common distance measure, respectively. Promising experimental results, in particular for some classes of objects, are obtained on real UAV images acquired over urban areas. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | Image analysis, local binary pattern (LBP), multilabeling classification, similarity measures, unmanned aerial vehicles (UAV) |
Field | DocType | ISSN |
Histogram,Computer science,Remote sensing,Artificial intelligence,Operator (computer programming),Tile,Binary number,Multiclass classification,Computer vision,Pattern recognition,Exploit,Image resolution,Grid | Conference | 2153-6996 |
Citations | PageRank | References |
1 | 0.39 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Moranduzzo | 1 | 43 | 2.30 |
| Mohamed Lamine Mekhalfi | 2 | 62 | 8.01 |
| Farid Melgani | 3 | 1100 | 80.98 |