A Novel 2D-3D CNN with Spectral-Spatial Multi-Scale Feature Fusion for Hyperspectral Image Classification Article Swipe
Dongxu Liu
,
Guangliang Han
,
Peixun Liu
,
Hang Yang
,
Xinglong Sun
,
Qingqing Li
,
Jiajia Wu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13224621
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13224621
Multifarious hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have been gradually proposed and achieve a promising classification performance. However, hyperspectral image classification still suffers from various challenges, including abundant redundant information, insufficient spectral-spatial representation, irregular class distribution, and so forth. To address these issues, we propose a novel 2D-3D CNN with spectral-spatial multi-scale feature fusion for hyperspectral image classification, which consists of two feature extraction streams, a feature fusion module as well as a classification scheme. First, we employ two diverse backbone modules for feature representation, that is, the spectral feature and the spatial feature extraction streams. The former utilizes a hierarchical feature extraction module to capture multi-scale spectral features, while the latter extracts multi-stage spatial features by introducing a multi-level fusion structure. With these network units, the category attribute information of HSI can be fully excavated. Then, to output more complete and robust information for classification, a multi-scale spectral-spatial-semantic feature fusion module is presented based on a Decomposition-Reconstruction structure. Last of all, we innovate a classification scheme to lift the classification accuracy. Experimental results on three public datasets demonstrate that the proposed method outperforms the state-of-the-art methods.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs13224621
- https://www.mdpi.com/2072-4292/13/22/4621/pdf?version=1637140672
- OA Status
- gold
- Cited By
- 29
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3213437176
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- OpenAlex ID
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https://openalex.org/W3213437176Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs13224621Digital Object Identifier
- Title
-
A Novel 2D-3D CNN with Spectral-Spatial Multi-Scale Feature Fusion for Hyperspectral Image ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-17Full publication date if available
- Authors
-
Dongxu Liu, Guangliang Han, Peixun Liu, Hang Yang, Xinglong Sun, Qingqing Li, Jiajia WuList of authors in order
- Landing page
-
https://doi.org/10.3390/rs13224621Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/13/22/4621/pdf?version=1637140672Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2072-4292/13/22/4621/pdf?version=1637140672Direct OA link when available
- Concepts
-
Hyperspectral imaging, Pattern recognition (psychology), Computer science, Artificial intelligence, Feature extraction, Feature (linguistics), Convolutional neural network, Fusion, Spatial analysis, Contextual image classification, Image (mathematics), Remote sensing, Geography, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
29Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 10, 2023: 8, 2022: 5Per-year citation counts (last 5 years)
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67Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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