Semantic segmentation of remote sensing images has attracted considerable attentions. For cross domain semantic segmentation, the images captured at different times inevitably exhibit significant domain gaps, which limits the segmentation performance of unlabelled domain. There are numerous methods to cope with these problems, while style transfer and domain adaptation are effective for domain gaps, the outcomes are still not ideal. Nearly all methods ignore the combination of image-level alignment and feature-level alignment, while few methods consider class-wise constraint to boost the performance. Towards this end, IFSDA, an image-level and feature-level semantic-aware architecture for cross domain semantic segmentation is put forward. In order to acquire sound outcomes, two branches of alignment strategies are realized by self-supervised learning and generative adversarial learning. Besides, a novel semantic discriminator is utilized in image translation process to optimize class-related information, thereby helping to eliminate the intra-class domain gaps between bi-temporal images and optimize the segmentation results effectively. Experiments on ISPRS 2D Semantic Labeling Contest Dataset have shown the superiority of proposed method over other models.