Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Cycle-Consistent Adversarial Networks

Semantic segmentation is an important computer vision task for the analysis of aerial imagery in many remote sensing applications. Due to the large availability of data it is possible to design efficient convolutional neural network based deep learning models for this purpose. But these methods usually show a weak performance when they are applied without any modifications to data from another domain with different characteristics relating to aspects concerning the sensor or environmental influences. To improve the performance of these methods domain adaptation approaches can be employed. In the following work, we want to present a method for unsupervised domain adaptation for semantic segmentation. We trained an encoder-decoder model on the source domain dataset as task application and adjusted the network to the target domain. The adaptation process is based on a style transfer component, which is realized using a cycle-consistent adversarial network. Through a continuous adaptation of the task model we achieved a higher generalization of the network and increased the task method performance on the target domain.