An Adaptive Surrogate-Assisted Endmember Extraction Framework Based on Intelligent Optimization Algorithms for Hyperspectral Remote Sensing Images Article Swipe
Zhao Wang
,
Jianzhao Li
,
Yiting Liu
,
Fei Xie
,
Peng Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14040892
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14040892
As the foremost step of spectral unmixing, endmember extraction has been one of the most challenging techniques in the spectral unmixing processing due to the mixing of pixels and the complexity of hyperspectral remote sensing images. The existing geometrial-based endmember extraction algorithms have achieved the ideal results, but most of these algorithms perform poorly when they do not meet the assumption of simplex structure. Recently, many intelligent optimization algorithms have been employed to solve the problem of endmember extraction. Although they achieved the better performance than the geometrial-based algorithms in different complex scenarios, they also suffer from the time-consuming problem. In order to alleviate the above problems, balance the two key indicators of accuracy and running time, an adaptive surrogate-assisted endmember extraction (ASAEE) framework based on intelligent optimization algorithms is proposed for hyperspectral remote sensing images in this paper. In the proposed framework, the surrogate-assisted model is established to reduce the expensive time cost of the intelligent algorithms by fitting the fully constrained evaluation value with the low-cost estimated value. In more detail, three commonly used intelligent algorithms, namely genetic algorithm, particle swarm optimization algorithm and differential evolution algorithm, are specifically designed into the ASAEE framework to verify the effectiveness and robustness. In addition, an adaptive weight surrogate-assisted model selection strategy is proposed, which can automatically adjust the weights of different surrogate models according to the characteristics of different intelligent algorithms. Experimental results on three data sets (including two simulated data sets and one real data set) show the effectiveness and the excellent performance of the proposed ASAEE framework.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14040892
- OA Status
- gold
- Cited By
- 16
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4213126894
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4213126894Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs14040892Digital Object Identifier
- Title
-
An Adaptive Surrogate-Assisted Endmember Extraction Framework Based on Intelligent Optimization Algorithms for Hyperspectral Remote Sensing ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-02-13Full publication date if available
- Authors
-
Zhao Wang, Jianzhao Li, Yiting Liu, Fei Xie, Peng LiList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14040892Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/rs14040892Direct OA link when available
- Concepts
-
Endmember, Hyperspectral imaging, Computer science, Robustness (evolution), Particle swarm optimization, Algorithm, Artificial intelligence, Data mining, Biochemistry, Gene, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
16Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2, 2023: 8, 2022: 4Per-year citation counts (last 5 years)
- References (count)
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50Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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