MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

Rui Li*, Chenxi Duan, Shunyi Zheng, Ce Zhang, Peter M. Atkinson

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

52 Citations (Scopus)

Abstract

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 multiscale features generated by different layers of U-Net and design a multiscale 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 multiscale 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 data sets captured by different satellite sensors demonstrate that the proposed MACU-Net transcends the U-Net, U-Netpyramid pooling layers (PPL), U-Net 3+, among other benchmark approaches. Code is available at https://github.com/lironui/MACU-Net.

Original languageEnglish
Article number8007205
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume19
DOIs
Publication statusPublished - 1 Feb 2021

Bibliographical note

Publisher Copyright:
© 2004-2012 IEEE.

Keywords

  • Asymmetric convolution block (ACB)
  • fine-resolution remotely sensed images
  • semantic segmentation

Fingerprint

Dive into the research topics of 'MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images'. Together they form a unique fingerprint.

Cite this