HFCC-Net: A Dual-Branch Hybrid Framework of CNN and CapsNet for Land-Use Scene Classification Article Swipe
Ningbo Guo
,
Mingyong Jiang
,
Lijing Gao
,
Kaitao Li
,
Fengjie Zheng
,
Xiangning Chen
,
Mingdong Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/rs15205044
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/rs15205044
Land-use scene classification (LUSC) is a key technique in the field of remote sensing imagery (RSI) interpretation. A convolutional neural network (CNN) is widely used for its ability to autonomously and efficiently extract deep semantic feature maps (DSFMs) from large-scale RSI data. However, CNNs cannot accurately extract the rich spatial structure information of RSI, and the key information of RSI is easily lost due to many pooling layers, so it is difficult to ensure the information integrity of the spatial structure feature maps (SSFMs) and DSFMs of RSI with CNNs only for LUSC, which can easily affect the classification performance. To fully utilize the SSFMs and make up for the insufficiency of CNN in capturing the relationship information between the land-use objects of RSI, while reducing the loss of important information, we propose an effective dual-branch hybrid framework, HFCC-Net, for the LUSC task. The CNN in the upper branch extracts multi-scale DSFMs of the same scene using transfer learning techniques; the graph routing-based CapsNet in the lower branch is used to obtain SSFMs from DSFMs in different scales, and element-by-element summation achieves enhanced representations of SSFMs; a newly designed function is used to fuse the top-level DSFMs with SSFMs to generate discriminant feature maps (DFMs); and, finally, the DFMs are fed into classifier. We conducted sufficient experiments using HFCC-Net on four public datasets. The results show that our method has better classification performance compared to some existing CNN-based state-of-the-art methods.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs15205044
- https://www.mdpi.com/2072-4292/15/20/5044/pdf?version=1697799766
- OA Status
- gold
- Cited By
- 7
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387821564
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387821564Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs15205044Digital Object Identifier
- Title
-
HFCC-Net: A Dual-Branch Hybrid Framework of CNN and CapsNet for Land-Use Scene ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-20Full publication date if available
- Authors
-
Ningbo Guo, Mingyong Jiang, Lijing Gao, Kaitao Li, Fengjie Zheng, Xiangning Chen, Mingdong WangList of authors in order
- Landing page
-
https://doi.org/10.3390/rs15205044Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/15/20/5044/pdf?version=1697799766Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-4292/15/20/5044/pdf?version=1697799766Direct OA link when available
- Concepts
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Computer science, Convolutional neural network, Artificial intelligence, Pattern recognition (psychology), Classifier (UML), Feature (linguistics), Data mining, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 4, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
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59Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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