Abstract: Objective: Domain adaptation classification of remote sensing images usually takes spectral features or simple spatial features as data features, and labels domains lacking labeled samples by aligning feature distributions among domains. The domain adaptation method ignores the deeper spatial features of remote sensing images, resulting in insufficient exploitation of spatially localized information. At the same time, the domain adaptation method seldom takes into account the problem of class imbalance, which leads to the importance of the small class of samples being ignored. Methods: To address the above problems, balanced metric learning in multilevel spatial features for domain adaptation in remote sensing image classification is proposed . First, deeper spatial features of remote sensing images are extracted by an iterative mean filter. Then, metric learning is constructed to align the marginal distributions and minimize the intraclass distance and interclass scatter. Finally, adaptive weights are constructed based on the prior probability of the classes to maximize the balanced interclass distances to alleviate the problem of class imbalance. Results: The proposed method first designed four tasks on the Worldview-2 dataset and Pavia dataset to evaluate the effectiveness. Then, the overall accuracy, kappa coefficient, visualization results, and time cost, which are commonly used in remote sensing image classification tasks, are used as the evaluation indicators. Secondly, a series of experiments are carried out to analyze the effects of different parameters on the classification results. Lastly, the individual components on the classification results. Compared with the suboptimal algorithm, the proposed method improves the overall accuracy by 1.64% to 9.18%, and the kappa coefficient by 0.0231 to 0.1717. Conclusions: The experimental results show that the proposed method not only enhances the extraction and effective utilization of local features through multilevel spatial features but also alleviates the problem of class imbalance effectively so that the classifier achieves a better prediction result.