Road extraction from optical remote sensing images has many important application scenarios, such as navigation, automatic driving and road network planning, etc. Current deep learning based models have achieved great successes in road extraction. Most deep learning models improve abilities rely on using deeper layers, resulting to the obese of the trained model. Besides, the training of a deep model is also difficult, and may be easy to fall into over fitting. Thus, this paper studies to improve the performance through combining multiple lightweight models. However, in fact multiple isolated lightweight models may perform worse than a deeper and larger model. The reason is that those models are trained isolated. To solve the above problem, we propose an Adaboost-like End-To-End Multiple Lightweight U-Nets model (AEML U-Nets) for road extraction. Our model consists of multiple lightweight U-Net parts. Each output of prior U-Net is as the input of next U-Net. We design our model as multiple-objective optimization problem to jointly train all the U-Nets. The approach is tested on two open datasets (LRSNY and Massachusetts) and Shaoshan dataset. Experimental results prove that our model has better performance compared with other state-of-the-art semantic segmentation methods.

从光学遥感图像提取道路具有许多重要的应用场景，例如导航，自动驾驶和道路网络规划等。当前基于深度学习的模型在道路提取中取得了巨大的成功。大多数深度学习模型依靠使用更深的层次来提高能力，从而导致训练后的模型变得肥胖。此外，深度模型的训练也很困难，并且可能容易陷入过度拟合。因此，本文研究通过组合多个轻量级模型来提高性能。但是，实际上，多个隔离的轻量级模型的性能可能不如更深，更大的模型。原因是那些模型是孤立训练的。为了解决上述问题，我们提出了一种类似于Adaboost的端到端多重轻量级U-Nets模型（AEML U-Nets）用于道路提取。我们的模型包含多个轻型U-Net零件。先前的U-Net的每个输出都作为下一个U-Net的输入。我们将模型设计为多目标优化问题，以共同训练所有U-Net。该方法在两个开放数据集（LRSNY和马萨诸塞州）和韶山数据集上进行了测试。实验结果证明，与其他最新的语义分割方法相比，我们的模型具有更好的性能。