Research on Image Segmentation Methods for High Resolution Building Images
To solve the segmentation problem of buildings in urban close range images and effectively improve image segmentation efficiency,the establishment of segmentation methods and accuracy analysis were carried out for buildings with different textures and different photographic distances.Conducted image segmentation experiments on multiple sets of building images using traditional methods and deep learning based image segmentation methods,and conducted visualization and segmentation accuracy analysis.The results have show that traditional image segmentation methods have lower computational complexity and faster processing speed.The average accuracy of segmentation for three types of signs is doors,windows,pipes,and walls is 36.8%,33.3%,and 53.0%,respectively.However,they are greatly affected by noise.The segmentation accuracy of deep learning based methods for three types of targets is 93.3%,76.7%,and 80.8%,respectively.The segmentation effect and accuracy are better than traditional methods,but it requires training a large number of parameters and strong computational resource support.
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