Aggregating Features From Dual Paths for Remote Sensing Image Scene Classification Article Swipe
Donghang Yu
,
Qing Xu
,
Haitao Guo
,
Jun Lu
,
Yuzhun Lin
,
Xiangyun Liu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/access.2022.3147543
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/access.2022.3147543
Scene classification is an important and challenging task employed toward understanding remote sensing images. Convolutional neural networks have been widely applied in remote sensing scene classification in recent years, boosting classification accuracy. However, with improvements in resolution, the categories of remote sensing images have become ever more fine-grained. The high intraclass diversity and interclass similarity are the main characteristics that differentiate remote scene image classification from natural image classification. To extract discriminative representation from images, we propose an end-to-end feature fusion method that aggregates features from dual paths (AFDP). First, lightweight convolutional neural networks with fewer parameters and calculations are used to construct a feature extractor with dual branches. Then, in the feature fusion stage, a novel feature fusion method that integrates the concepts of bilinear pooling and feature connection is adopted to learn discriminative features from images. The AFDP method was evaluated on three public remote sensing image benchmarks. The experimental results indicate that the AFDP method outperforms current state-of-the-art methods, with advantages of simple form, strong versatility, fewer parameters, and less calculation.
Related Topics
Concepts
Computer science
Artificial intelligence
Discriminative model
Pattern recognition (psychology)
Convolutional neural network
Feature extraction
Contextual image classification
Feature (linguistics)
Pooling
Image fusion
Boosting (machine learning)
Computer vision
Image (mathematics)
Philosophy
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2022.3147543
- https://ieeexplore.ieee.org/ielx7/6287639/9668973/09696349.pdf
- OA Status
- gold
- Cited By
- 13
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4210404147
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4210404147Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2022.3147543Digital Object Identifier
- Title
-
Aggregating Features From Dual Paths for Remote Sensing Image Scene ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Donghang Yu, Qing Xu, Haitao Guo, Jun Lu, Yuzhun Lin, Xiangyun LiuList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2022.3147543Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/9668973/09696349.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/9668973/09696349.pdfDirect OA link when available
- Concepts
-
Computer science, Artificial intelligence, Discriminative model, Pattern recognition (psychology), Convolutional neural network, Feature extraction, Contextual image classification, Feature (linguistics), Pooling, Image fusion, Boosting (machine learning), Computer vision, Image (mathematics), Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 6, 2023: 3, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
66Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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