Attention-Based Multiscale Residual Adaptation Network for Cross-Scene Classification

In recent years, classification has obtained ever-rising attention and has been applied to many areas in the field of remote sensing, including land use, forest monitoring, urban planning, and vegetation management. Due to the lack of labeled data and the poor generalization ability of supervised models, cross-scene classification is proposed for better utilization of the existing knowledge. Existing adaptation methods for cross-scene classification only consider the marginal distribution, while the conditional distribution is equally important in real applications. In addition, approaches based on deep learning align the distribution of features extracted from a single-scale structure, leading to the loss of information. To overcome the above drawbacks, an Attention-based Multiscale Residual Adaptation Network (AMRAN) is proposed for cross-scene classification tasks. In the proposed AMRAN, both the marginal and conditional distributions are taken into consideration for more comprehensive alignment. Besides, the attention mechanism and the multiscale strategy are used to extract more robust features and more complete information, respectively. Experimental results between four existing scene classification data sets demonstrate that AMRAN has a significant improvement compared with the state-of-the-art deep adaptation methods.

Automated summary

This study proposes an Attention-based Multiscale Residual Adaptation Network (AMRAN) for cross-scene classification tasks, which achieves better performance by considering both marginal and conditional distributions for comprehensive feature alignment.