基于粒子群算法的多尺度反卷积特征融合的道路提取

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.