遥感影像中建筑物的Unet分割改进

Segmentation of Buildings in Remote Sensing Images by Improved Unet Algorithm

针对经典Unet算法在提取遥感影像中建筑物特征时存在编码信息丢失、对多尺度建筑目标适应性差和上下文特征联系不足的问题,本研究提出了一种多尺度融合的变形残差金字塔编解码网络.首先,引入深度编码网络与下采样旁路网络替换原编码结构,共同完成对建筑物目标高阶特征信息的提取;其次,在编码网络次末端节点引入联合变形卷积的残差金字塔结构,以提升网络对建筑物多尺度特征和边缘模糊特征的辨识能力;最后,将高阶和低阶特征逐层级联融合,在解码网络末端获取对建筑物的分割结果.实验结果表明,改进后模型相比原模型在F1-score和MIOU指标上分别提升了1.6%和2.1%.

The loss of coding information, the poor adaptability to multi-scale building targets, and the insufficient contextual feature connection can be found in the classic Unet algorithm during the extraction of building features from remote sensing images. To tackle these problems, this study proposes a deformed-residual-pyramid codec network with multi-scale fusion. First, the original coding structure is replaced by the deep coding network and the down-sampling bypass network, which jointly extract the high-level feature information of the building target. Second, the residual pyramid structure combined with deformed convolution is introduced at the penultimate node of the coding network to improve the network’s ability to recognize multi-scale features and edge fuzzy features of buildings. Finally, the high-and low-level features are cascaded and merged layer by layer, and the segmentation result of the building is obtained at the end of the decoding network. The experimental results show that compared with the original model, the improved model has increased F1-score and MIoU by 1.6% and 2.1%, respectively.