北京测绘2024，Vol.38Issue(9)：1346-1351,6.DOI:10.19580/j.cnki.1007-3000.2024.09.019
基于轻量网络的遥感影像建筑物提取
Building extraction from remote sensing images based on lightweight network
摘要
Abstract
Existing building extraction algorithms focus on the improvement of accuracy while ignoring the increase in model computation amount and parameters.To address this issue,a lightweight network model for building extraction from high-resolution remote sensing images was designed.The model was based on the U-Net structure,and a hybrid convolutional unit consisting of depthwise separable convolution and ordinary convolution was used to build the model,so as to reduce the computation amount and parameters of the model.At the same time,a lightweight dual attention module was added behind each unit of the model to enhance the feature extraction capability of the model and improve the building extraction accuracy,realizing a balance between performance and spatiotemporal complexity.The experimental results on Satellite dataset Ⅱ datasets show that the intersection over union(IoU)and F1 score of the lightweight network model reach 0.696 4 and 0.821 1,which are 4.45%and 3.18%higher than those of the U-Net model,respectively.The amount of computation and parameters are reduced by 34.56%and 44.79%compared with those of the U-Net model,which results in a significant improvement in overall performance.In terms of extraction effect,the model has better extraction results than other neural network models when facing the interference of complex backgrounds,small buildings,and surrounding features.关键词
遥感影像/建筑物提取/轻量网络/深度可分离卷积/注意力模块Key words
remote sensing image/building extraction/lightweight network/depthwise separable convolution/attention module分类
天文与地球科学引用本文复制引用
陈振,张小青,周文娟..基于轻量网络的遥感影像建筑物提取[J].北京测绘,2024,38(9):1346-1351,6.基金项目
福建省中青年教师教育科研项目(JAT220574) （JAT220574）
福建省教育科学"十四五"规划2022年度课题. （）
