改进的遥感图像语义分割算法

An improved semantic segmentation algorithm for remote sensing images

针对遥感图像中多个目标聚集导致边缘混淆,小尺度物体分割不明显,以及语义分割过程中全局信息获取不足的问题,提出了一种基于混合注意力与全尺度跳层连接网络的遥感图像语义分割算法DU-net。该算法以U-net3+为基础网络,采用全尺度跳层连接网络作为特征提取网络,摒弃了原算法中的深度监督,建立特征与注意力机制之间的关联,最终实现语义分割的过程。实验结果表明,DU-net算法在不同指标下较经典算法都有明显提升,同时提高了图像边缘分割质量,改善了算法对小尺度目标的分割准确度。

Aiming at the problems of edge confusion caused by multiple objects gathering in remote sensing image, unclear segmentation of small scale objects, and insufficient global information acquisition in semantic segmentation process, this paper proposes a semantic segmentation algorithm of remote sensing images based on mixed attention and full-scale skip connection network, called DU-net. In this algorithm, U-net3+ is used as the basic network, and full-scale skip connection network is used as the feature extraction network. The depth supervision in the original model is abandoned, the association between feature and attention mechanism is established, and the process of semantic segmentation is finally realized. The experimental results show that the DU-net algorithm has significant improvement over the classical algorithm under different indexes, and improves the quality of image edge segmentation and the accuracy of the algorithm for small scale target segmentation.