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.
LBP-based multiclass classification method for UAV imagery
Mohamed L. Mekhalfi;
2015-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.