MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images. (arXiv:2007.13083v3 [eess.IV] UPDATED)

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 …

arxiv images segmentation semantic