A unified architecture for super-resolution and segmentation of remote sensing images based on similarity feature fusion

The resolution of the image has an important impact on the accuracy of segmentation. Integrating super-resolution (SR) techniques in the semantic segmentation of remote sensing images contributes to the improvement of precision and accuracy, especially when the images are blurred. In this paper, a novel and efficient SR semantic segmentation network (SRSEN) is designed by taking advantage of the similarity between SR and segmentation tasks in feature processing. SRSEN consists of the multi-scale feature encoder, the SR fusion decoder, and the multi-path feature refinement block, which adaptively establishes the feature associations between segmentation and SR tasks to improve the segmentation accuracy of blurred images. Experiments show that the proposed method achieves higher segmentation accuracy on fuzzy images compared to state-of-the-art models. Specifically, the mIoU of the proposed SRSEN is 3%–6% higher than other state-of-the-art models on low-resolution LoveDa, Vaihingen, and Potsdam datasets.