Abstract

The rapid technological advancement manifested lately in the remote sensing acquisition platforms has triggered many benefits in favor of automated territory control and monitoring. In particular, unmanned aerial vehicles (UAVs) technology has drawn a lot of attention, providing an efficient solution especially in real-time applications. This is mainly motivated by their capacity to collect extremely high resolution (EHR) data over inaccessible areas and limited coverage zones, thanks to their small size and rapidly deployable flight capability, notwithstanding their ease of use and affordability. The very high level of details of the data acquired via UAVs, however, in order to be properly availed, requires further treatment through suitable image processing and analysis approaches. In this respect, the proposed methodological contributions in this thesis include: i) a complete processing chain which assists the Avalanche Search and Rescue (SAR) operations by scanning the UAV acquired images over the avalanche debris in order to detect victims buried under snow and their related objects in real time; ii) two multilabel deep learning strategies for coarsely describing extremely high resolution images in urban scenarios; iii) a novel multilabel conditional random fields classification framework that exploits simultaneously spatial contextual information and cross-correlation between labels; iv) a novel spatial and structured support vector machine for multilabel image classification by adding to the cost function of the structured support vector machine a term that enhances spatial smoothness within a one-step process. Conducted experiments on real UAV images are reported and discussed alongside suggestions for potential future improvements and research lines.