"FPA-Net: Frequency-Guided Position-Based Attention Network for Land Co" by Al Shahriar Rubel and Frank Y. Shih
 

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

Document Type

Article

Publication Date

9-15-2023

Abstract

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.

Identifier

85174424120 (Scopus)

Publication Title

International Journal of Pattern Recognition and Artificial Intelligence

External Full Text Location

https://doi.org/10.1142/S0218001423540150

e-ISSN

17936381

ISSN

02180014

Issue

11

Volume

37

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