Multi-stage attention ResU-Net for semantic segmentation of fine-resolution remote sensing images

Li, Rui; Zheng, Shunyi; Duan, Chenxi; Su, Jianlin; Zhang, Ce ORCID: https://orcid.org/0000-0001-5100-3584. 2022 Multi-stage attention ResU-Net for semantic segmentation of fine-resolution remote sensing images. IEEE Geoscience and Remote Sensing Letters, 19, 8009205. 5, pp. 10.1109/LGRS.2021.3063381

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Official URL: http://dx.doi.org/10.1109/LGRS.2021.3063381

Abstract/Summary

The attention mechanism can refine the extracted feature maps and boost the classification performance of the deep network, which has become an essential technique in computer vision and natural language processing. However, the memory and computational costs of the dot-product attention mechanism increase quadratically with the spatiotemporal size of the input. Such growth hinders the usage of attention mechanisms considerably in application scenarios with large-scale inputs. In this letter, we propose a linear attention mechanism (LAM) to address this issue, which is approximately equivalent to dot-product attention with computational efficiency. Such a design makes the incorporation between attention mechanisms and deep networks much more flexible and versatile. Based on the proposed LAM, we refactor the skip connections in the raw U-Net and design a multistage attention ResU-Net (MAResU-Net) for semantic segmentation from fine-resolution remote sensing images. Experiments conducted on the Vaihingen data set demonstrated the effectiveness and efficiency of our MAResU-Net. Our code is available at https://github.com/lironui/MAResU-Net.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1109/LGRS.2021.3063381
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-)
ISSN:	1545-598X
Additional Keywords:	semantic segmentation, fine-resolution remote sensing images, linear attention mechanism
NORA Subject Terms:	Ecology and Environment Computer Science Data and Information
Date made live:	11 Apr 2022 09:54 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/532465

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1109/LGRS.2021.3063381

UKCEH and CEH Sections/Science Areas:

Soils and Land Use (Science Area 2017-)

ISSN:

1545-598X

Additional Keywords:

semantic segmentation, fine-resolution remote sensing images, linear attention mechanism

NORA Subject Terms:

Ecology and Environment
Computer Science
Data and Information