Learning aerial image similarity using triplet networks

The main way to determine the location of an aircraft is to use a GPS signal. However, this method works inaccurately when communication with satellites is lost or contaminated with interference, so it is important to explore alternative ways to locate an aircraft. One such method is explored in this research paper, which is to use a particle filter algorithm to search for the location of the aircraft based on the orthographic images taken by the aircraft. This algorithm must be able to determine whether two orthographic photographs are similar or not. For this task, a triplet neural network with ResNet base layers is used. Its accuracy is compared to the accuracy of the neural network with VGG-16 base layers. A dataset of Vilnius orthographic images is created to train the neural network and to perform experiments. Drone flight simulations are used to determine which parameter values provide the highest accuracy of the drone location recognition algorithm

Learning aerial image similarity using triplet networks

Today unmanned aerial vehicles (UAV) are becoming increasingly used. For their safe travel they use GPS connection, but in recent years GPS blocking technology has improved. For this reason UAV should have a way to navigate without any external signals. One solution is to use a camera built into the UAV that is pointing to the ground. The photos taken by this camera are compared to the systems‘ known maps and by finding similarities between them determine the geographical location of the UAV. Part of this solution was explored in the research work, where the neural network VGG16 was used to discover similarities between orthographic photos. Another part of the solution was explored in another research paper, which proposed the use of a particle filter to solve the problem of UAV localization. In this paper, an algorithm will be developed based on particle filter and DenseNet-121 neural network to solve unmanned aerial vehicles localization problem. A new dataset of orthographic image triplets will be also developed to train the neural network