An Alternating Training Method of Attention-Based Adapters for Visual Explanation of Multi-Domain Satellite Images

Recently, satellite image analytics based on convolutional neural networks have been vigorously investigated; however, in order for the artificial intelligence systems to be applied in practice, there still exists several challenges: (a) model explanability to improve the reliability of the artificial intelligence system by providing the evidence for the prediction results; (b) dealing with domain shift among images captured by multiple satellites of which the specification of the image sensors is various. To resolve the two issues in the development of a deep model for satellite image analytics, in this paper we propose a multi-domain learning method based on attention-based adapters. As plug-ins to the backbone network, the adapter modules are designed to extract domain-specific features as well as improve visual attention for input images. In addition, we also discuss an alternating training strategy of the backbone network and the adapters in order to effectively separate domain-invariant features and -specific features, respectively. Finally, we utilize Grad-CAM/LIME to provide visual explanation on the proposed network architecture. The experimental results demonstrate that the proposed method can be used to improve test accuracy, and its enhancement in visual explanability is also validated.