Comparison of different feature detectors and descriptors for car classification in UAV images

Unmanned aerial vehicles (UAV) are among the fast growing remote sensing technologies in these last few years. This is mainly because UAVs allow acquiring images characterized by an extremely high spatial resolution and they exhibit an interesting operational flexibility. Taking advantage from these unique characteristics can help in addressing problems typical of the civilian contexts. In particular, identifying and monitoring cars inside an urban environment is viewed as an important and challenging problem because it could limit issues related to traffic jams and pollution. In this work, we investigate the use of several detectors and descriptors to find the best representation of cars for their classification in UAV images. Experimental results on real UAV images are reported and discussed.