Multiclass Coarse Analysis for UAV Imagery

This paper presents a novel method to “coarsely” describe extremely high-resolution (EHR) images acquired by means of unmanned aerial vehicles (UAVs) over urban areas. Standard image analysis approaches cannot be directly exploited for the automatic description of UAV images due to their EHR. For this reason, we propose an alternative approach that consists first in the subdivision of the original UAV image in a grid of tiles. Then, each tile is compared with a library of training tiles to inherit the binary multilabel vector of the most similar training tile. This vector conveys a list of classes likely present in the considered tile. Our multiclass tile-based approach needs the definition of two main ingredients: 1) a suitable tile-representation strategy; and 2) a tile-to-tile matching operation. Various tile-representation and matching strategies are investigated. In particular, we present three global representation strategies, which process each tile as a whole and two point-based strategies that exploit points of interest within the considered tile. Regarding the matching strategies, two simple measures of distance, namely, the Euclidean and the chi-squared histogram distances, are explored. Interesting experimental results conducted on a rich set of real UAV images acquired over an urban area are reported and discussed.