MACU-Net Semantic Segmentation from High-Resolution Remote Sensing Images

Semantic segmentation of remote sensing images plays an important role in land resource management, yield estimation, and economic assessment. U-Net is a sophisticated encoder-decoder architecture which has been frequently used in medical image segmentation and has attained prominent performance. And asymmetric convolution block can enhance the square convolution kernels using asymmetric convolutions. In this paper, based on U-Net and asymmetric convolution block, we incorporate multi-scale features generated by different layers of U-Net and design a multi-scale skip connected architecture, MACU-Net, for semantic segmentation using high-resolution remote sensing images. Our design has the following advantages: (1) The multi-scale skip connections combine and realign semantic features contained both in low-level and high-level feature maps with different scales; (2) the asymmetric convolution block strengthens the representational capacity of a standard convolution layer. Experiments conducted on two remote sensing image datasets captured by separate satellites demonstrate that the performance of our MACU-Net transcends the U-Net, SegNet, DeepLab V3+, and other baseline algorithms.