Abstract:
Building detection system through the remote sensing of images has been widely
studied. In this thesis, we propose a model for detecting buildings at airports in Asia
through different levels of remote sensing image. The proposed model is improved using
the You Only Look Once (YOLO) algorithm based on the convolutional neural network
(CNN). We also adjust an inputted image to our model using the Jet Saliency Map. The
buildings to be detected in this study are the passenger terminals, the control towers,
the cargo buildings, and the hangars. The data set has been collected from 322 different
airports in Asia. Furthermore, our improved model is also examined for efficiency and
accuracy. The results show that it can detect the intended objects efficiently and provides
higher accuracy than the original model.
