Unsupervised domain adaption for remote sensing semantic segmentation with self-attention mechanism

The domain shift between the source and target domains limits the performance of traditional convolutional neural networks (CNNs) for feature extraction in remote sensing tasks. We propose an image translation network that uses generative adversarial networks (GANs) to transfer spectral distributions from training to test data, enhancing cross-domain semantic segmentation. Our approach fine-tunes the DeepLab-V3 framework on synthetic training data generated by the proposed network. Experimental results show improved performance in cross-domain semantic segmentation tasks for remote sensing images.