  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.
