Autoencoder-Based Hyperspectral Unmixing with Simultaneous Number-of-Endmembers Estimation Article Swipe
Atheer Abdullah Alshahrani
,
Ouiem Bchir
,
Mohamed Maher Ben Ismail
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25082592
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25082592
Hyperspectral unmixing plays a fundamental role in mining meaningful information from hyperspectral data. It promotes advancements in various scientific, environmental, and industrial applications by extracting meaningful information from hyperspectral data. However, it is still hindered by several challenges, including accurately identifying the number of endmembers in a hyperspectral image, extracting the endmembers, and estimating their abundance fractions. This research addresses these challenges by employing a convolutional-neural-network-based autoencoder that leverages both the spatial and spectral information present in the hyperspectral image. Additionally, a self-learning module utilizing a fuzzy clustering algorithm is designed to determine the number of endmembers. A novel approach is also introduced that estimates the abundances of the endmembers from the autoencoder and the clustering output. Real datasets and relevant performance metrics were used to validate and evaluate the performance of the proposed method. The results demonstrate that our approach outperforms related methods, achieving improvements of 47% in Spectral Angle Distance (SAD) and 42% in root-mean-square error (RMSE).
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s25082592
- OA Status
- gold
- Cited By
- 1
- References
- 71
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409606511
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409606511Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s25082592Digital Object Identifier
- Title
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Autoencoder-Based Hyperspectral Unmixing with Simultaneous Number-of-Endmembers EstimationWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-19Full publication date if available
- Authors
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Atheer Abdullah Alshahrani, Ouiem Bchir, Mohamed Maher Ben IsmailList of authors in order
- Landing page
-
https://doi.org/10.3390/s25082592Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/s25082592Direct OA link when available
- Concepts
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Hyperspectral imaging, Autoencoder, Pattern recognition (psychology), Computer science, Cluster analysis, Artificial intelligence, Convolutional neural network, Mean squared error, Abundance estimation, Artificial neural network, Data mining, Mathematics, Abundance (ecology), Statistics, Fishery, BiologyTop 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)
- References (count)
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71Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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