Semantic segmentation is an essential part of deep learning. In recent years, with the development of remote sensing big data, semantic segmentation has been increasingly used in remote sensing. Deep convolutional neural networks (DCNNs) face the challenge of feature fusion: very-high-resolution remote sensing image multisource data fusion can increase the network’s learnable information, which is conducive to correctly classifying target objects by DCNNs; simultaneously, the fusion of high-level abstract features and low-level spatial features can improve the classification accuracy at the border between target objects. In this paper, we propose a multipath encoder structure to extract features of multipath inputs, a multipath attention-fused block module to fuse multipath features, and a refinement attention-fused block module to fuse high-level abstract features and low-level spatial features. Furthermore, we propose a novel convolutional neural network architecture, named attention-fused network (AFNet). Based on our AFNet, we achieve state-of-the-art performance with an overall accuracy of 91.7% and a mean F1 score of 90.96% on the ISPRS Vaihingen 2D dataset and an overall accuracy of 92.1% and a mean F1 score of 93.44% on the ISPRS Potsdam 2D dataset.

语义分割是深度学习的重要组成部分。近年来，随着遥感大数据的发展，语义分割已越来越多地应用于遥感中。深度卷积神经网络（DCNNs）面临特征融合的挑战：超高分辨率遥感图像多源数据融合可以增加网络的可学习信息，有利于DCNNs正确分类目标对象；同时，高级抽象特征和低级空间特征的融合可以提高目标对象边界处的分类精度。在本文中，我们提出了一个多路径编码器结构来提取多路径输入的特征，一个多路径注意力融合块模块来融合多路径特征，和一个细化注意力融合块模块来融合高级抽象特征和低级空间特征。此外，我们提出了一种新颖的卷积神经网络架构，称为注意力融合网络（AFNet）。基于我们的 AFNet，我们在 ISPRS Vaihingen 2D 数据集上以 91.7% 的整体准确度和 90.96% 的平均 F1 分数以及 92.1% 的整体准确度和 93.44 的平均 F1 分数实现了最先进的性能% 在 ISPRS Potsdam 2D 数据集上。