Abstract
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.
Highlights
According to the United Nations (UN)1, more than 55.3% of the world’s population lived in urban areas in 2018, and the number is still growing
This work trains a deep network in the source domain, predicts labels of instances from the target domain with the trained network, selects reliable predictions in the target domain based on defined criterion, and tunes the trained network with the selected reliable samples
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B2-2020, 2020 XXIV ISPRS Congress (2020 edition) et al and Liu et al have both applied a generative adversarial network strategy to deal with domain adaptation for land cover mapping using very high resolution (VHR) optical aerial images
Summary
According to the United Nations (UN), more than 55.3% of the world’s population lived in urban areas in 2018, and the number is still growing. Some efforts have been done toward providing detailed urban maps on the global scale (Demuzere et al, 2019; Yoo et al, 2019) All those studies have pointed out a technical issue for achieving their goals, the cross-city classification challenge. This work trains a deep network in the source domain, predicts labels of instances from the target domain with the trained network, selects reliable predictions in the target domain based on defined criterion, and tunes the trained network with the selected reliable samples The selection of reliable predictions in this framework requires human interaction and empirical experiences It might be an issue in practice when dealing with big data. It is very expensive to access VHR optical aerial images with a consistent quality or a global coverage
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