计算机科学 ›› 2021, Vol. 48 ›› Issue (8): 162-168.doi: 10.11896/jsjkx.200700182
王施云, 杨帆
WANG Shi-yun, YANG Fan
摘要： 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。
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