基于粒子群算法的多尺度反卷积特征融合的道路提取

Translated title of the contribution: Road Extraction Based on PSO Different Ratio Deconvolution Feature Fusion

Feng Pan, Qi Chao An, Qi Diao, Rui Wang, Xiao Xue Feng

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

To improve road extraction accuracy in complex scenes based on traditional FCN algorithm with different scales of multi-scale feature fusion,several works were carried out for the complex aerial road scene, designing a FROBIT farmland road dataset for farmland environment, extracting the road information from FROBIT dataset (farmland road) and Massachusetts road dataset (city road) based on full convolutional neural network (FCN), improving the deconvolution method based on traditional FCN network, implementing multi-scale feature fusion with different proportions based on particle swarm optimization (PSO). Comparing the multi-scale FCN network proposed in this paper with the traditional FCN neural network on the FROBIT dataset and the Massachusetts road dataset, the experimental results show that the multi-scale FCN network is superior to the traditional FCN neural network in extraction accuracy.

Translated title of the contributionRoad Extraction Based on PSO Different Ratio Deconvolution Feature Fusion
Original languageChinese (Traditional)
Pages (from-to)640-647
Number of pages8
JournalBeijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume40
Issue number6
DOIs
Publication statusPublished - 1 Jun 2020

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