PEGNet: Progressive Edge Guidance Network for Semantic Segmentation of Remote Sensing Images

Abstract

Owing to the rapid development of deep neural networks, prominent advances have been recently achieved in the semantic segmentation of remote sensing images. As the vital components of computer vision, semantic segmentation, and edge detection have strong correlation whether in the extracted features or task objective. Prior studies treated edge detection as a postprocessing operation to semantic segmentation, or they implicitly combined the two tasks. We consider that pixels around the edges are easy to be misdivided because of the prevalence of intraclass inconsistencies and interclass indistinctions, which reflect the discriminative ability of models to distinguish different classes. In this letter, we propose a multipath atrous module to first enrich the deep semantic information. Then, we combine the enhanced deep semantic information and dilated edge information generated by canny and morphological operations to obtain edge-region maps via edge-region detection module, which identifies pixels around the edges. Then, we relearn these error-prone pixels using a guidance module for the segmentation branch in a progressive guided manner. Combined with edge and segmentation branches, our progressive edge guidance network achieves an overall accuracy of 91.0% on the ISPRS Vaihingen test set, which is the new state-of-the-art result.