FPA-Net: Frequency-Guided Position-Based Attention Network for Land Cover Image Segmentation

Al Shahriar Rubel, Frank Y. Shih

Research output: Contribution to journalArticlepeer-review


Land cover segmentation has been a significant research area because of its multiple applications including the infrastructure development, forestry, agriculture, urban planning, and climate change research. In this paper, we propose a novel segmentation method, called Frequency-guided Position-based Attention Network (FPA-Net), for land cover image segmentation. Our method is based on encoder-decoder improved U-Net architecture with position-based attention mechanism and frequency-guided component. The position-based attention block is used to capture the spatial dependency among different feature maps and obtain the relationship among relevant patterns across the image. The frequency-guided component provides additional support with high-frequency features. Our model is simple and efficient in terms of time and space complexities. Experimental results on the Deep Globe, GID-15, and Land Cover AI datasets show that the proposed FPA-Net can achieve the best performance in both quantitative and qualitative measures as compared against other existing approaches.

Original languageEnglish (US)
Article number2354015
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number11
StatePublished - Sep 15 2023

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


  • Land cover segmentation
  • U-Net
  • attention mechanism
  • encoder-decoder
  • frequency-guided component
  • residual connection


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