Fully Convolutional Siamese Autoencoder for Change Detection in UAV Aerial Images

Abstract

Different applications in remote sensing, such as crop monitoring and visual surveillance, demand the automatic detection of changes from sets of images acquired over time. Most traditional approaches use satellite imagery, which, besides the known issues such as cloud cover and image acquisition frequency for nongeostationary satellites, are very costly. In this context, with the recent technological advances, unmanned aerial vehicles (UAVs) have become ubiquitous in numerous applications. In this letter, we present a fully convolutional Siamese autoencoder method for change detection in aerial images, in particular for those obtained with UAVs. We show that, by using an autoencoder, we can further reduce the number of labeled samples required to achieve competitive results. We evaluated the performance of our approach on two different data sets, and the results showed that our methodology outperforms the state of the art, while demanding less training data.