A method for information extraction of buildings from remote sensing images based on hybrid attention mechanism and Deeplabv3+

LIU Chenchen; GE Xiaosan; WU Yongbin; YU Haikun; ZHANG Beibei

doi:10.6046/zrzyyg.2023295

2025 Vol. 37, No. 1

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LIU Chenchen, GE Xiaosan, WU Yongbin, YU Haikun, ZHANG Beibei. 2025. A method for information extraction of buildings from remote sensing images based on hybrid attention mechanism and Deeplabv3+. Remote Sensing for Natural Resources, 37(1): 31-37. doi: 10.6046/zrzyyg.2023295

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LIU Chenchen, GE Xiaosan, WU Yongbin, YU Haikun, ZHANG Beibei. 2025. A method for information extraction of buildings from remote sensing images based on hybrid attention mechanism and Deeplabv3+. Remote Sensing for Natural Resources, 37(1): 31-37. doi: 10.6046/zrzyyg.2023295

A method for information extraction of buildings from remote sensing images based on hybrid attention mechanism and Deeplabv3+

1. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China;
2. Henan Surveying and Mapping Geographic Information Technology Center, Zhengzhou 450003, China;
3. Henan Remote Sensing Institute, Zhengzhou 450003, China

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Corresponding author: GE Xiaosan

Received Date: 22 September 2023

Revised Date: 26 January 2024

Abstract

Abstract

Extracting information about buildings from a large and complex set of remote sensing images has always been a hot research topic in the intelligent applications of remote sensing. To address issues such as inaccurate information extraction of buildings and the tendency to ignore small buildings within a complex environment in remote sensing images, this study proposed the SC-deep network-a semantic segmentation algorithm for remote sensing images based on a hybrid attention mechanism and Deeplabv3+. Utilizing an encoder-decoder structure, this network employs a backbone residual attention network to extract deep- and shallow-layer features. Meanwhile, this network aggregates the spatial and channel information weights in remote sensing images using a dilated space pyramid pool module and a channel-space attention module. These allow for effectively utilizing the multi-scale information of building structures in remote sensing images, thereby reducing the loss of image details during training. The experimental results indicate that the proposed method outperforms other mainstream segmentation networks on the Aerial imagery dataset. Overall, this method can effectively identify and extract the edges of complex buildings and small structures, exhibiting superior building extraction performance.
- multi-scale information /
- building extraction /
- semantic segmentation /
- attention mechanisms /
- dilated convolution

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References

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