Name
Abstract | ||
|---|---|---|
In this paper, we present a multilabel classification method for images acquired by means of Unmanned Ariel Vehicles (UAV) over urban areas. Due to the fact that UAV-grabbed images are characterized by extremely high spatial resolution, usual recognition schemes (such as traditional satellite or airborne based images) are likely to fail. In this work, tile-based multilabel classification framework is adopted to overcome such issue. In particular, a given UAV-shot image is first subdivided into a grid of equal tiles. Next, deep neural network-induced features are extracted from each tile and then fed into a radial basis function neural network classifier in order to infer the corresponding object list. We apply a refinement step at the top of the complete deep network architecture to boost the classification results. The proposed method was evaluated on a dataset acquired over the city of Trento, Italy with an hexacopter UAV. Superior classification rates have been scored with respect to the state-of-the-art. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGARSS.2016.7730325 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
unmanned aerial vehicle, extremely high resolution, image multilabeling, multi-object detection, image analysis, coarse description | Computer science,Convolutional neural network,Remote sensing,Network architecture,Artificial intelligence,Classifier (linguistics),Tile,Computer vision,Satellite,Pattern recognition,Feature extraction,Image resolution,Grid | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdallah Zeggada | 1 | 43 | 4.12 |
| Farid Melgani | 2 | 1100 | 80.98 |