MULTILABEL CLASSIFICATION OF UAV IMAGES WITH CONVOLUTIONAL NEURAL NETWORKS

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.