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

doi:10.6046/zrzyyg.2023295

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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.

Keywords multi-scale information building extraction semantic segmentation attention mechanisms dilated convolution

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TP79

Issue Date: 17 February 2025

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	Chenchen LIU
	Xiaosan GE
	Yongbin WU
	Haikun YU
	Beibei ZHANG

Cite this article:

Chenchen LIU,Xiaosan GE,Yongbin WU, et al. A method for information extraction of buildings from remote sensing images based on hybrid attention mechanism and Deeplabv3+[J]. Remote Sensing for Natural Resources, 2025, 37(1): 31-37.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023295 OR https://www.gtzyyg.com/EN/Y2025/V37/I1/31

Fig.1 Attention residual block

Tab.1 CAM-Resnet50 network structure

Fig.2 Channel attention module

Fig.3 Spatial attention module

Fig.4 SC-deep network

Fig.5 Image preprocessing results

Tab.2 Experiment environment configuration

Tab.3 Results of backbone network ablation experiments (%)

Tab.4 Results of attention module ablation experiments (%)

Tab.5 Comparative experiment segmentation visualization results

Tab.6 Compare experimental results (%)

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