We can draw the conclusion that UDA methods in the field of remote sensing data are carried out later than those applied in natural images, and due to the domain gap caused by appearance differences, most of methods focus on how to use generative training (GT) methods to improve the model’s performance. Meanwhile, we sort out definitions and methodology introductions of partial, open-set and multi-domain UDA, which are more pertinent to real-world remote sensing applications. Moreover, remote sensing applications are introduced by a thorough dataset analysis. Thus, in this paper, in order to explore the further progress and development of UDA methods in remote sensing, based on the analysis of the causes of domain shift, a comprehensive review is provided with a fine-grained taxonomy of UDA methods applied for remote sensing data, which includes Generative training, Adversarial training, Self-training and Hybrid training methods, to better assist scholars in understanding remote sensing data and further advance the development of methods. There are a lot of reviews that have elaborated on UDA methods based on natural data, but few of these studies take into consideration thorough remote sensing applications and contributions. Unsupervised Domain Adaptation (UDA) is one of the solutions to the aforementioned problems of labeled data defined as the source domain and unlabeled data as the target domain, i.e., its essential purpose is to obtain a well-trained model and tackle the problem of data distribution discrepancy defined as the domain shift between the source and target domain. However, the model trained on existing data cannot be directly used to handle the new remote sensing data, and labeling the new data is also time-consuming and labor-intensive. With the rapid development of the remote sensing monitoring and computer vision technology, the deep learning method has made a great progress to achieve applications such as earth observation, climate change and even space exploration. Based on these results, STDAN can be effectively applied to automated cross-domain crop type mapping without analyst intervention when prior information is available in the target domain. In particular, the superiority of STDAN was shown to be prominent when the domain discrepancy was substantial. In most cases, the classification performance of STDAN was found to be compatible with the classification using training data collected from the target domain. The potential of STDAN was evaluated by conducting six experiments reflecting various domain discrepancy conditions in unmanned aerial vehicle images acquired at different regions and times. STDAN consists of three analysis stages: (1) initial classification using domain adversarial neural networks (2) the self-training-based updating of training candidates using constraints specific to crop classification and (3) the refinement of training candidates using iterative classification and final classification. The core purpose of STDAN is to combine adversarial training to alleviate spectral discrepancy problems with self-training to automatically generate new training data in the target domain using an existing thematic map or ground truth data. This study presents self-training with domain adversarial network (STDAN), a novel unsupervised domain adaptation framework for crop type classification. Classification based on unsupervised domain adaptation, which uses prior information from the source domain for target domain classification, can solve the impractical problem of collecting sufficient training data. However, the main obstacle to generating annual crop type maps is the collection of sufficient training data for supervised classification. Automated crop type mapping using remote sensing images is preferred for the consistent monitoring of crop types. Crop type mapping is regarded as an essential part of effective agricultural management.
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