MIGN: Multiscale Image Generation Network for Remote Sensing Image Semantic Segmentation

With the development of computer vision, the semantic segmentation of remote sensing images, which has become an important topic, has been utilized in various applications for image content analysis and understanding, such as urban planning, natural disaster monitoring, and land resource management. Many approaches have been proposed to address these problems. However, due to obvious differences in resolution, spatial structure, and semantics between remote sensing images and ordinary images, the semantic segmentation of remote sensing images is still challenging. In this paper, we propose a novel multiscale image generation network (MIGN) that can efficiently generate high-resolution segmentation results by considering both details and boundary information. In particular, a multi-attention mechanism method for semantic segmentation of remote sensing images is designed. The attention weight is calculated by capturing the interaction of cross dimensions in a two-branch structure, which can learn the underlying feature information and guarantee the performance of each pixel feature for final classification. We also propose an edge supervised module to ensure that the segmentation boundary has a more accurate performance. A multiscale image fusion algorithm based on the Bayes model is proposed to improve the accuracy of the segmentation module. The performance of our model is evaluated on the ISPRS Vaihingen and Potsdam datasets. The results show that our method is superior to the most advanced image segmentation methods in terms of MIoU and pixel accuracy.