SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGE BASED ON IMPROVED U-NET
Semantic segmentation of remote sensing images is to classify each pixel in the image according to the type of land cover.It is an important research direction in the field of remote sensing image processing.The segmentation is inaccurate due to similar features in the research process.In order to solve this problem,DeepResU-Net,a remote sensing image semantic segmentation network based on U-Net and residual network is proposed.It improved the traditional U-Net semantic segmentation network,used U-Net as the skeleton network,and used residual convolution unit to replace the convolutional layer in the coding layer and decoding layer of the original U-Net,so as to prevent the network gradient from disappearing.The network contained rich jump connections that could promote information dissemination.Experiments on the remote sensing(ISPRS)Vaihingen dataset show that the results obtained by this method are more accurate than FCN-8s,SegNet,U-Net,and ResU-Net.
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