摘要
为解决高分辨图像分割中存在的普遍问题,文章提出了一种基于U-Net改进的建筑物分割算法。在编码器中利用迁移学习解决标记数据集的稀缺性问题,在跳过连接中引入特征细化块来细化特征,在解码器中加入通道注意力机制选择更多具有辨别力的特征,算法在Inria航拍图像标记数据集上验证可行性。
To solve the common problems in high-resolution image segmentation,this paper propose an improved building segmentation algorithm based on U-Net.Transfer learning is used in the encoder to solve the scarcity problem of labeled datasets,a feature refi nement module is introduced in skip connections to refi ne features,and a channel attention mechanism is added in the decoder to select more discriminative features.The algorithm is validated on the Inria aerial image labeling dataset.
作者
叶焕然
周润
YE Huanran;ZHOU Run(School of Mechanical and Electrical Information,Yiwu Industrial and Commercial College,Jinhua 322000,China)
基金
金华市科协学术研究项目2022JKX37。
关键词
遥感图像
建筑物分割
注意力机制
U-Net
remote sensing image
building segmentation
attention mechanism
U-Net