Road network extraction from satellite images is widely applicated in
intelligent traffic management and autonomous driving fields. The
high-resolution remote sensing images contain complex road areas and distracted
background, which make it a challenge for road extraction. In this study, we
present a stacked multitask network for end-to-end segmenting roads while
preserving connectivity correctness. In the network, a global-aware module is
introduced to enhance pixel-level road feature representation and eliminate
background distraction from overhead images; a road-direction-related
connectivity task is added to ensure that the network preserves the graph-level
relationships of the road segments. We also develop a stacked multihead
structure to jointly learn and effectively utilize the mutual information
between connectivity learning and segmentation learning. We evaluate the
performance of the proposed network on three public remote sensing datasets.
The experimental results demonstrate that the network outperforms the
state-of-the-art methods in terms of road segmentation accuracy and
connectivity maintenance.

本研究提出了一个堆叠多任务网络，在保持连通性正确性的同时，端到端地分割道路。在网络中，引入了全局感知模块来增强像素级道路特征表示并消除空中图像的干扰背景；增加了与道路方向相关的连通性任务，以确保网络保持道路片段的图级关系。我们还开发了一种堆叠多头结构，以共同学习和有效利用连通性学习和分割学习之间的相互信息。我们在三个公共遥感数据集上评估了所提出网络的性能。实验结果表明，该网络在道路分割准确性和连通性维护方面优于现有方法。

通过连接性和分割的联合学习进行道路网络的细粒度提取

Fine-Grained Extraction of Road Networks via Joint Learning of  Connectivity and Segmentation

We propose a novel method for unsupervised semantic image segmentation based
on mutual information maximization between local and global high-level image
features. The core idea of our work is to leverage recent progress in
self-supervised image representation learning. Representation learning methods
compute a single high-level feature capturing an entire image. In contrast, we
compute multiple high-level features, each capturing image segments of one
particular semantic class. To this end, we propose a novel two-step learning
procedure comprising a segmentation and a mutual information maximization step.
In the first step, we segment images based on local and global features. In the
second step, we maximize the mutual information between local features and
high-level features of their respective class. For training, we provide solely
unlabeled images and start from random network initialization. For quantitative
and qualitative evaluation, we use established benchmarks, and COCO-Persons,
whereby we introduce the latter in this paper as a challenging novel benchmark.
InfoSeg significantly outperforms the current state-of-the-art, e.g., we
achieve a relative increase of 26% in the Pixel Accuracy metric on the
COCO-Stuff dataset.

本文提出了一种基于自监督学习的无监督语义图像分割的新方法，通过局部和全局高级别图像特征间的互信息最大化计算多个高级别特征，实现对不同语义类别的图像分割和互信息最大化的两阶段学习过程，并在已有数据集和引入的 COCO-Persons 数据集上相对推进了 26%（基于像素准确率评估）。