Semantic segmentation models based on deep learning have shown remarkable performance in road extraction from high-resolution aerial images. However, it is still a difficult task to segment multiscale roads with high completeness and accuracy from complex backgrounds. To deal with this problem, this letter proposes an end to end network named Multiple features integrated with convolutional long-short time memory unit network (MFI-CLSTMN). First, in MFI-CLSTMN, the ConvLSTM unit is designed to explore and integrate the sequential correlations among features, which can alleviate the feature loss caused by the max-pooling operation. Second, the structure of dense concatenation and multiscale up-sampling combines detailed features with semantic information to preserve road details. At last, at the optimization stage, a self-adaptive composite loss function is added to handle class imbalance, such that MFI-CLSTMN can effectively train hard examples and avoids local optimum. Experiments demonstrate that MFI-CLSTMN has higher segmentation accuracy and lower computational complexity than four comparative state-of-the-art models in a consistent environment. Moreover, MFI-CLSTMN can especially protect road segmentation from netsplit and brokenness, which is hard for other models to achieve.

基于深度学习的语义分割模型在高分辨率航空图像的道路提取中表现出卓越的性能。但是，从复杂背景中分割具有高完整性和准确性的多尺度道路仍然是一项艰巨的任务。为了解决这个问题，这封信提出了一种端到端网络，该网络名为卷积长时短时存储单元网络（MFI-CLSTMN），集成了多种功能。首先，在MFI-CLSTMN中，ConvLSTM单元旨在探究和整合特征之间的顺序相关性，从而可以减轻由最大合并操作引起的特征损失。其次，密集级联和多尺度上采样的结构将详细特征与语义信息相结合，以保留道路细节。最后，在优化阶段，添加了自适应复合损失函数以处理类不平衡，从而使MFI-CLSTMN可以有效地训练困难的示例，并避免局部最优。实验证明，在一致的环境中，MFI-CLSTMN比四个比较的最新技术模型具有更高的分割精度和更低的计算复杂度。此外，MFI-CLSTMN可以特别保护道路分割免受网裂和断裂的影响，这是其他模型难以实现的。