House extraction from railway remote sensing images based on improved Mask R-CNN model
In response to the low efficiency of manual visual remote sensing interpretation and long production cycle in the production of topographic maps during the pre-feasibility study stage of railway construction,this paper combined the characteristics and advantages of deep learning to improve the instance segmentation model Mask R-CNN(Mask Region based Convolutional Neural Network).The paper selected ResNeXt101 as its backbone feature extraction network,and further implemented automatic vectorization extraction of remote sensing images using edge extraction algorithms.The experimental results show that the improved model has significant improvements in indicators such as Mask AP50,Box AP50,Mask mAP,and Box mAP.It can generate vectorized images of houses for the calculation of demolition costs in the pre-feasibility stage of railway construction,provide technical support for image processing work in this stage.
万方数据
维普
