| Popis: |
Due to the high spatial resolution of high-resolution remote sensing images,rich ground objects information,high complexity,uneven distribution of target categories and different sizes of various ground objects,it is difficult to improve the segmentation accuracy.In order to improve the semantic segmentation accuracy of remote sensing images and solve the problem that U-Net model is limited when combining deep semantic information and shallow position information,a semantic segmentation me-thod of remote sensing images based on U-Net feature fusion optimization strategy is proposed.This method adopts the encoder-decoder structure based on U-Net network.In the feature extraction part of the network,the encoder structure of U-Net model is used to extract the feature information of multiple layers.In the feature fusion part,the jump connection structure of U-Net is retained,and at the same time,the feature fusion optimization strategy proposed in this paper is used to realize the fusion-optimization-refusion of high-level semantic features and low-level location features.In addition,the feature fusion optimization strategy uses dilated convolution to get more global features,and uses Sub-Pixel convolutional layer instead of traditional transposed convolution to achieve adaptive upsampling.This method is validated on the Potsdam dataset and Vaihingen dataset of ISPRS.The three evaluation indexes,overall classification accuracy,Kappa coefficient and mIoU in the verification are 86.2%,0.82,0.77 on Potsdam dataset,and 84.5%,0.79,0.69 on Vaihingen dataset.Compared with the traditional U-Net model,the three evaluation indicators are increased by 5.8%,8%,8% on Potsdam dataset,and 3.5%,4%,11% on Vaihingen dataset.Experimental results show that the remote sensing image semantic segmentation method based on the U-Net feature fusion optimization strategy has achieved good semantic segmentation effects on both the Potsdam dataset and the Vaihingen dataset,which can improve the accuracy of semantic segmentation of remote sensing images. |
