Hybridizing Cross-Level Contextual and Attentive Representations for Remote Sensing Imagery Semantic Segmentation Article Swipe
Xin Li
,
Feng Xu
,
Runliang Xia
,
Xin Lyu
,
Hongmin Gao
,
Yao Tong
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13152986
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13152986
Semantic segmentation of remote sensing imagery is a fundamental task in intelligent interpretation. Since deep convolutional neural networks (DCNNs) performed considerable insight in learning implicit representations from data, numerous works in recent years have transferred the DCNN-based model to remote sensing data analysis. However, the wide-range observation areas, complex and diverse objects and illumination and imaging angle influence the pixels easily confused, leading to undesirable results. Therefore, a remote sensing imagery semantic segmentation neural network, named HCANet, is proposed to generate representative and discriminative representations for dense predictions. HCANet hybridizes cross-level contextual and attentive representations to emphasize the distinguishability of learned features. First of all, a cross-level contextual representation module (CCRM) is devised to exploit and harness the superpixel contextual information. Moreover, a hybrid representation enhancement module (HREM) is designed to fuse cross-level contextual and self-attentive representations flexibly. Furthermore, the decoder incorporates DUpsampling operation to boost the efficiency losslessly. The extensive experiments are implemented on the Vaihingen and Potsdam benchmarks. In addition, the results indicate that HCANet achieves excellent performance on overall accuracy and mean intersection over union. In addition, the ablation study further verifies the superiority of CCRM.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs13152986
- https://www.mdpi.com/2072-4292/13/15/2986/pdf
- OA Status
- gold
- Cited By
- 29
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3189528951
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- OpenAlex ID
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https://openalex.org/W3189528951Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs13152986Digital Object Identifier
- Title
-
Hybridizing Cross-Level Contextual and Attentive Representations for Remote Sensing Imagery Semantic SegmentationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-07-29Full publication date if available
- Authors
-
Xin Li, Feng Xu, Runliang Xia, Xin Lyu, Hongmin Gao, Yao TongList of authors in order
- Landing page
-
https://doi.org/10.3390/rs13152986Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/13/15/2986/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/13/15/2986/pdfDirect OA link when available
- Concepts
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Computer science, Segmentation, Artificial intelligence, Convolutional neural network, Discriminative model, Representation (politics), Pixel, Pattern recognition (psychology), Politics, Law, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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29Total citation count in OpenAlex
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2025: 8, 2024: 8, 2023: 9, 2022: 4Per-year citation counts (last 5 years)
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48Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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