基于粒子群算法的多尺度反卷积特征融合的道路提取

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

科研成果: 期刊稿件文章同行评审

2 引用 (Scopus)

摘要

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.

投稿的翻译标题Road Extraction Based on PSO Different Ratio Deconvolution Feature Fusion
源语言繁体中文
页(从-至)640-647
页数8
期刊Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
40
6
DOI
出版状态已出版 - 1 6月 2020

关键词

  • Full convolutional neural network
  • Pattern feature optimization
  • Semantic segmentation
  • Unstructured road

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引用此

Pan, F., An, Q. C., Diao, Q., Wang, R., & Feng, X. X. (2020). 基于粒子群算法的多尺度反卷积特征融合的道路提取. Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology, 40(6), 640-647. https://doi.org/10.15918/j.tbit1001-0645.2019.198