EVALUATION OF UNet AND UNet++ ARCHITECTURES IN HIGH RESOLUTION IMAGE CHANGE DETECTION APPLICATIONS
Change detection applications from satellite imagery can be a very useful tool in monitoring human activities and understanding their interaction with the physical environment. In the past few years most of the recent research approaches to automatic change detection have been based on the application of Deep Learning techniques and especially on variations of Convolutional Neural Network architectures due to their great representational capacity and their state-of-the-art performance in visual tasks such as image classification and semantic segmentation. In this work we train and evaluate two CNN architectures, UNet and UNet++, on a change detection task using Very High-Resolution satellite images collected at two different time epochs. We also examine and analyse the effect of two different loss functions, a combination of the Binary Cross Entropy Loss with the Dice Loss, and the Lovász Hinge loss, both of which were specifically designed for semantic segmentation applications. Finally, we experiment with the use of data augmentation as well as deep supervision techniques to evaluate and quantify their contribution in the final classification performance of the different network architectures.