论文标题
MACU网络，用于细分遥感图像的语义分割
MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images
论文作者
论文摘要
远程感知图像的语义细分在土地资源管理，收益估算和经济评估中起着重要作用。 U-NET是一种深层编码器体系结构，经常以高精度用于图像分割。在这封信中，我们结合了由U-NET不同层产生的多尺度特征，并设计了多尺度的跳过连接和基于不对称的U-NET（MACU-NET），以使用精细分辨率远程感知的图像进行分割。我们的设计具有以下优点：（1）低级和高级功能图中包含的多尺度跳过连接组合和重新调整语义特征； （2）不对称卷积块增强了标准卷积层的特征表示和特征提取能力。在两个由不同卫星传感器捕获的远程感知的数据集上进行的实验表明，所提出的MACU-NET超越了U-NET，U-NETPPL，U-NET 3+，以及其他基准方法。代码可在https://github.com/lironui/macu-net上找到。
Semantic segmentation of remotely sensed images plays an important role in land resource management, yield estimation, and economic assessment. U-Net, a deep encoder-decoder architecture, has been used frequently for image segmentation with high accuracy. In this Letter, we incorporate multi-scale features generated by different layers of U-Net and design a multi-scale skip connected and asymmetric-convolution-based U-Net (MACU-Net), for segmentation using fine-resolution remotely sensed images. Our design has the following advantages: (1) The multi-scale skip connections combine and realign semantic features contained in both low-level and high-level feature maps; (2) the asymmetric convolution block strengthens the feature representation and feature extraction capability of a standard convolution layer. Experiments conducted on two remotely sensed datasets captured by different satellite sensors demonstrate that the proposed MACU-Net transcends the U-Net, U-NetPPL, U-Net 3+, amongst other benchmark approaches. Code is available at https://github.com/lironui/MACU-Net.
