基于U-Net特征融合优化策略的遥感影像语义分割方法

Remote Sensing Image Semantic Segmentation Method Based on U-Net Feature Fusion Optimization Strategy

高分辨率遥感影像的空间分辨率高、地物信息丰富、复杂程度高、各类地物的大小尺寸不一,这为分割精度的提高带来了一定的难度。为提高遥感影像语义分割精度,解决U-Net模型在结合深层语义信息与浅层位置信息时受限的问题,文中提出了一种基于U-Net特征融合优化策略的遥感影像语义分割方法。该方法采用基于U-Net模型的编码器-译码器结构,在特征提取部分沿用U-Net模型的编码器结构,提取多个层级的特征信息;在特征融合部分保留U-Net的跳跃连接结构,同时使用提出的特征融合优化策略,实现了高层语义特征与底层位置特征的融合-优化-再融合。此外特征融合优化策略还使用空洞卷积获取了更多的全局特征,并采用Sub-Pixel卷积层代替传统转置卷积,实现了自适应上采样。所提方法在ISPRS的Potsdam数据集和Vaihingen数据集上得到了验证,其总体分割精度、Kappa系数和平均交并比mIoU 3个评价指标在Potsdam数据集上分别为86.2%,0.82,0.77,在Vaihingen数据集上分别为84.5%,0.79,0.69;相比传统的U-Net模型,所提方法的3个评价指标在Potsdam数据集上分别提高了5.8%,8%,8%,在Vaihingen数据集上分别提高了3.5%,4%,11%。实验结果表明,基于U-Net特征融合优化策略的遥感影像语义分割方法,在Potsdam数据集和Vaihingen数据集上都能达到很好的语义分割效果,提高了遥感影像的语义分割精度。

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.