Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (8): 105-116.DOI: 10.3778/j.issn.1002-8331.2203-0564

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

Regularized Extraction of Remotely Sensed Image Buildings Using U-Shaped Networks

DAI Chao, LIU Ping, SHI Juncai, REN Hongjie   

  1. College of Big Data, Taiyuan University of Technolgy, Jinzhong, Shanxi 030600, China
  • Online:2023-04-15 Published:2023-04-15



  1. 太原理工大学 大数据学院,山西 晋中 030600

Abstract: Aiming at the problem that the bilinear interpolation and transposed convolution algorithm of fully convolutional neural network cannot accurately restore the outline of the segmented object in the task of high-resolution remote sensing image building extraction, an improved ResNeXt_SPP_Unet fully convolutional neural network is established based on the Unet network, and it proposes improved Douglas Peucker image post-processing algorithm to complete building extraction regularization. The ResNeXt_SPP_Unet network focuses on two aspects of optimization. One is to replace the standard convolution in Unet with ResNeXt Block, which reduces the number of model operations and improves the segmentation accuracy of the network; the other is to introduce the SPP pyramid pooling layer at the end of the Encoder. The method of scale feature fusion improves the segmentation accuracy of object edges. Through experimental comparison and analysis, the results show that the improved ResNeXt_SPP_Unet is superior to classical segmentation networks such as Unet and cutting-edge segmentation networks such as ResUnet++ in the task of high-scoring remote sensing image building extraction, with an average intersection ratio of 0.853 8 and an average pixel accuracy of 0.935 9. Finally, after connecting the improved Douglas Peucker algorithm to the ResNeXt_SPP_Unet model, the algorithm is improved by adding processing operations such as rotation and connection of the edge of the building outline, further fitting the real outline of the building, and regularizing the boundary of the building. It works well.

Key words: buildings, ResNeXt_Unet, deep learning, GF-2, Douglas Peucker

摘要: 针对全卷积神经网络双线性插值及转置卷积算法在高分遥感影像建筑物提取任务中无法准确还原分割对象轮廓的问题,基于Unet网络建立改进的ResNeXt_SPP_Unet全卷积神经网络,并提出改进Douglas Peucker图像后处理算法完成建筑物提取规则化。ResNeXt_SPP_Unet网络重点优化两个方面,一是将Unet中的标准卷积替换为ResNeXt Block,在减少模型运算数量的同时提高网络的分割精度;二是在Encoder末尾阶段引入SPP金字塔池化层,以多尺度特征融合的方式提升对象边缘的分割准确度。经实验对比分析,结果表明在高分遥感影像建筑物提取任务中,改进ResNeXt_SPP_Unet优于Unet等经典分割网络及ResUnet++等前沿分割网络,平均交并比达到了0.853?8,平均像素准确率达到了0.935?9。最后,将改进的Douglas Peucker算法衔接于ResNeXt_SPP_Unet模型之后,通过增加对建筑物轮廓边缘的旋转及连接等处理操作改进该算法,进一步拟合建筑物真实轮廓,对建筑物的边界进行规则化校正,效果良好。

关键词: 建筑物, ResNeXt_Unet, 深度学习, GF-2, Douglas Peucker