TY - JOUR
T1 - 基于粒子群算法的多尺度反卷积特征融合的道路提取
AU - Pan, Feng
AU - An, Qi Chao
AU - Diao, Qi
AU - Wang, Rui
AU - Feng, Xiao Xue
N1 - Publisher Copyright:
© 2020, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - 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.
AB - 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.
KW - Full convolutional neural network
KW - Pattern feature optimization
KW - Semantic segmentation
KW - Unstructured road
UR - http://www.scopus.com/inward/record.url?scp=85088129957&partnerID=8YFLogxK
U2 - 10.15918/j.tbit1001-0645.2019.198
DO - 10.15918/j.tbit1001-0645.2019.198
M3 - 文章
AN - SCOPUS:85088129957
SN - 1001-0645
VL - 40
SP - 640
EP - 647
JO - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
IS - 6
ER -