Spectral-Spatial Center-Aware Bottleneck Transformer for Hyperspectral Image Classification Article Swipe
Meng Zhang
,
Yi Yang
,
Sixian Zhang
,
Pengbo Mi
,
Deqiang Han
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16122152
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16122152
Hyperspectral image (HSI) contains abundant spectral-spatial information, which is widely used in many fields. HSI classification is a fundamental and important task, which aims to assign each pixel a specific class label. However, the high spectral variability and the limited labeled samples create challenges for HSI classification, which results in poor data separability and makes it difficult to learn highly discriminative semantic features. In order to address the above problems, a novel spectral-spatial center-aware bottleneck Transformer is proposed. First, the highly relevant spectral information and the complementary spatial information at different scales are integrated to reduce the impact caused by the high spectral variability and enhance the HSI’s separability. Then, the feature correction layer is designed to model the cross-channel interactions, thereby promoting the effective cooperation between different channels to enhance overall feature representation capability. Finally, the center-aware self-attention is constructed to model the spatial long-range interactions and focus more on the neighboring pixels that have relatively consistent spectral-spatial properties with the central pixel. Experimental results on the common datasets show that compared with the state-of-the-art classification methods, S2CABT has the better classification performance and robustness, which achieves a good compromise between the complexity and the performance.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs16122152
- https://www.mdpi.com/2072-4292/16/12/2152/pdf?version=1718289067
- OA Status
- gold
- Cited By
- 1
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399626427
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399626427Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs16122152Digital Object Identifier
- Title
-
Spectral-Spatial Center-Aware Bottleneck Transformer for Hyperspectral Image ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-13Full publication date if available
- Authors
-
Meng Zhang, Yi Yang, Sixian Zhang, Pengbo Mi, Deqiang HanList of authors in order
- Landing page
-
https://doi.org/10.3390/rs16122152Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/16/12/2152/pdf?version=1718289067Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/16/12/2152/pdf?version=1718289067Direct OA link when available
- Concepts
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Hyperspectral imaging, Computer science, Pixel, Discriminative model, Artificial intelligence, Pattern recognition (psychology), Robustness (evolution), Spatial analysis, Remote sensing, Geography, Gene, Biochemistry, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-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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