LBP-based multiclass classification method for UAV imagery

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.