A2-FPN for semantic segmentation of fine-resolution remotely sensed images

Rui Li, Libo Wang, Ce Zhang, Chenxi Duan*, Shunyi Zheng

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

12 Citations (Scopus)
82 Downloads (Pure)


The thriving development of earth observation technology makes more and more high-resolution remote-sensing images easy to obtain. However, caused by fine-resolution, the huge spatial and spectral complexity leads to the automation of semantic segmentation becoming a challenging task. Addressing such an issue represents an exciting research field, which paves the way for scene-level landscape pattern analysis and decision-making. To tackle this problem, we propose an approach for automatic land segmentation based on the Feature Pyramid Network (FPN). As a classic architecture, FPN can build a feature pyramid with high-level semantics throughout. However, intrinsic defects in feature extraction and fusion hinder FPN from further aggregating more discriminative features. Hence, we propose an Attention Aggregation Module (AAM) to enhance multiscale feature learning through attention-guided feature aggregation. Based on FPN and AAM, a novel framework named Attention Aggregation Feature Pyramid Network ((Formula presented.) -FPN) is developed for semantic segmentation of fine-resolution remotely sensed images. Extensive experiments conducted on four datasets demonstrate the effectiveness of our (Formula presented.) -FPN in segmentation accuracy. Code is available at https://github.com/lironui/A2-FPN.

Original languageEnglish
Pages (from-to)1131-1155
Number of pages25
JournalInternational journal of remote sensing
Issue number3
Early online date26 Feb 2022
Publication statusPublished - Feb 2022


  • attention mechanism
  • deep learning
  • semantic segmentation
  • UT-Hybrid-D


Dive into the research topics of 'A2-FPN for semantic segmentation of fine-resolution remotely sensed images'. Together they form a unique fingerprint.

Cite this