Research On Gf-2 Image Extraction Based On Deep Learning

In high-resolution remote sensing images,due to the high complexity of urban surface buildings,it is always difficult to extract buildings based on remote sensing images directly in image information analysis.Due to the limitations of object-oriented building extraction methods in practical applications,such as low extraction accuracy and large post-processing workload,the technology of extracting buildings based on deep learning algorithms has emerged in various building extraction algorithms in recent years.Currently,most deep learning methods use variants of fully convolutional networks(FCN),such as U-Net,Seg Net,and Rest Net.These networks have the advantages of significantly improving model performance and abstract feature extraction capabilities,so that they can accurately complete image segmentation and recognition.In order to make full use of the global and local information of buildings in high-resolution images to more accurately segment and extract buildings,this paper proposes a corrected neural network model based on boundary constraints on a fully convolutional neural network to make the boundary of the building extracted More clear and complete.The model is composed of a shared back-end and multi-task prediction module.It uses a modified U-Net and multi-task framework to generate segmentation graph prediction values and contours based on the shared features of the shared back-end.By specifying the boundary information,the model performance is improved.The improved model is applied to the international open source WHU building data set,and compared with the traditional U-Net,Seg Net,Rest Net models.Experiments show that the improved model has an average accuracy of more than 92.19% for building extraction in the image.Compared with traditional U-Net,Seg Net,and Rest Net models,the accuracy is improved by 37.28%,4.09%,and 1.86%,respectively.In this paper,the GF-2 image of Jiujiang area in Jiangxi Province is selected.Through manual preprocessing,image cropping,sub-regional statistics,forward rotation,and mirror flip to increase the original data and other preprocessing,a grid label form and a vector boundary form are established.Multi-format building dataset.In this paper,the data is divided into a sample set composed of 224×224 and 512×512 pictures,and the sample set is subdivided into three types of areas: main urban area,rural area and mixed area.By comparing the accuracy of the extraction results of the U-Net,Seg Net and Rest Net models with the improved network model proposed in this paper,the accuracy of building extraction in different areas with different specifications of pictures is explored.The experimental results show that the average accuracy of building extraction in the main urban area can reach more than 84.17% in the 224×224 image collection.In order to further highlight the accuracy of the boundary correction network model for building extraction,this paper breaks through the limitations of traditional research based on RGB images only,using: 1)RGB band combined picture sets of remote sensing images,2)NIRRG band combined picture sets,3)A mixed picture set of the first two types of three-band combined pictures to extract the buildings in the pictures,thereby making the most of the GF-2 image band.The experimental results show that the average accuracy of the building extraction results based on the four-band information is improved by 1.87% on the two types of specifications pictures based on the four-band information compared with the traditional building extraction results that only use three-band information And 1.74%.The boundary correction network proposed in this paper can effectively improve the accuracy of urban building information extraction and provide an effective way for the largescale extraction of urban building information.