RAANet: A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images Article Swipe
Runrui Liu
,
Fei Tao
,
Xintao Liu
,
Jiaming Na
,
Hongjun Leng
,
Junjie Wu
,
Tong Zhou
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14133109
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14133109
Classification of land use and land cover from remote sensing images has been widely used in natural resources and urban information management. The variability and complex background of land use in high-resolution imagery poses greater challenges for remote sensing semantic segmentation. To obtain multi-scale semantic information and improve the classification accuracy of land-use types in remote sensing images, the deep learning models have been wildly focused on. Inspired by the idea of the atrous-spatial pyramid pooling (ASPP) framework, an improved deep learning model named RAANet (Residual ASPP with Attention Net) is constructed in this paper, which constructed a new residual ASPP by embedding the attention module and residual structure into the ASPP. There are 5 dilated attention convolution units and a residual unit in its encoder. The former is used to obtain important semantic information at more scales, and residual units are used to reduce the complexity of the network to prevent the disappearance of gradients. In practical applications, according to the characteristics of the data set, the attention unit can select different attention modules such as the convolutional block attention model (CBAM). The experimental results obtained from the land-cover domain adaptive semantic segmentation (LoveDA) and ISPRS Vaihingen datasets showed that this model can enhance the classification accuracy of semantic segmentation compared to the current deep learning models.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14133109
- https://www.mdpi.com/2072-4292/14/13/3109/pdf?version=1656416031
- OA Status
- gold
- Cited By
- 115
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4290981008
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https://openalex.org/W4290981008Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs14133109Digital Object Identifier
- Title
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RAANet: A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
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2022-06-28Full publication date if available
- Authors
-
Runrui Liu, Fei Tao, Xintao Liu, Jiaming Na, Hongjun Leng, Junjie Wu, Tong ZhouList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14133109Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/14/13/3109/pdf?version=1656416031Direct link to full text PDF
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- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-4292/14/13/3109/pdf?version=1656416031Direct OA link when available
- Concepts
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Computer science, Residual, Artificial intelligence, Segmentation, Deep learning, Pyramid (geometry), Remote sensing, Pattern recognition (psychology), Computer vision, Geography, Algorithm, Physics, OpticsTop concepts (fields/topics) attached by OpenAlex
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115Total citation count in OpenAlex
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2025: 34, 2024: 39, 2023: 31, 2022: 11Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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