CTMU-Net: An Improved U-Net for Semantic Segmentation of Remote-Sensing Images Based on the Combined Attention Mechanism

With the development of remote-sensing technology, it is important to use semantic segmentation methods to obtain detailed information in remote-sensing images. However, the objects in the images reveal significant intraclass differences and slight interclass differences, thus affecting the acquisition of terrain information. To tackle the problems, this article proposes an improved U-Net for the semantic segmentation of remote-sensing images. First, the local importance-based pooling is introduced to alleviate the loss of feature details in the coding part. Second, a combined attention module with a double-branch structure is designed, which models the local relationship and the global relationship at the same time to obtain more typical features. Finally, in order to make full use of the feature information extracted from the coding part, the combined attention module and the channel attention module are added to different positions in U-Net. In order to validate the proposed method, we conduct experiments on the WHDLD dataset and compare the experimental results with other semantic segmentation methods. On the WHDLD dataset, the MPA, MIOU, and FWIOU of the proposed method reach 76$\%$, 64.11$\%$, and 75.64$\%$, respectively, revealing its priority. To demonstrate the generalization of the proposed method, generalization experiments are conducted via the LandCover.ai dataset and the Massachusetts-building dataset. The simulation results testify that the proposed method provides an excellent generalization ability.