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.

中文翻译：

---

PEGNet：渐进式边缘引导网络，用于遥感图像的语义分割

由于深度神经网络的迅速发展，近来在遥感图像的语义分割方面已经取得了显着的进步。作为计算机视觉的重要组成部分，无论是在提取的特征还是任务目标中，语义分割和边缘检测都具有很强的相关性。先前的研究将边缘检测视为语义分割的后处理操作，或者将它们隐式地结合了这两个任务。我们认为，由于类内不一致和类间不相容的普遍性，边缘周围的像素很容易被细分，这反映了模型区分不同类的判别能力。在这封信中，我们提出了一个多路径异常模块，以首先丰富深层语义信息。然后，我们将增强的深层语义信息与通过canny和形态学操作生成的扩张后的边缘信息相结合，以通过边缘区域检测模块获得边缘区域图，该模块可识别边缘周围的像素。然后，我们以逐步引导的方式使用用于分割分支的引导模块来重新学习这些容易出错的像素。与边缘和分割分支相结合，我们的渐进式边缘引导网络在ISPRS Vaihingen测试仪上的整体精度达到了91.0％，这是最新的最新结果。