UNSUPERVISED DOMAIN ADAPTATION USING A TEACHER-STUDENT NETWORK FOR CROSS-CITY CLASSIFICATION OF SENTINEL-2 IMAGES
- 1Signal processing in Earth observation (SiPEO), Technische Universität München (TUM), 80333 Munich, Germany
- 2Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Weßling, Germany
Keywords: Cross-city classification, Deep learning, Mean teacher model, Teacher-student network, Transfer learning, Unsupervised domain adaptation
Abstract. A machine learning algorithm in remote sensing often fails in the inference of a data set which has a different geographic location than the training data. This is because data of different locations have different underlying distributions caused by complicated reasons, such as the climate and the culture. For a large scale or a global scale task, this issue becomes relevant since it is extremely expensive to collect training data over all regions of interest. Unsupervised domain adaptation is a potential solution for this issue. Its goal is to train an algorithm in a source domain and generalize it to a target domain without using any label from the target domain. Those domains can be associated to geographic locations in remote sensing. In this paper, we attempt to adapt the unsupervised domain adaptation strategy by using a teacher-student network, mean teacher model, to investigate a cross-city classification problem in remote sensing. The mean teacher model consists of two identical networks, a teacher network and a student network. The objective function is a combination of a classification loss and a consistent loss. The classification loss works within the source domain (a city) and aims at accomplishing the goal of classification. The consistent loss works within the target domain (another city) and aims at transferring the knowledge learned from the source to the target. In this paper, two cross-city scenarios are set up. First, we train the model with the data of the city Munich, Germany, and test it on the data of the city Moscow, Russia. The second one is carried out by switching the training and testing data. For comparison, the baseline algorithm is a ResNet-18 which is also chosen as the backbone for the teacher and student networks in the mean teacher model. With 10 independent runs, in the first scenario, the mean teacher model has a mean overall accuracy of 53.38% which is slightly higher than the mean overall accuracy of the baseline, 52.21%. However, in the second scenario, the mean teacher model has a mean overall accuracy of 62.71% which is 5% higher than the mean overall accuracy of the baseline, 57.76%. This work demonstrates that it is worthy to explore the potential of the mean teacher model to solve the domain adaptation issues in remote sensing.