Multi-level quantum inspired metaheuristics for automatic clustering of hyperspectral images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10252704" target="_blank" >RIV/61989100:27240/23:10252704 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/23:10252704
Result on the web
<a href="https://link.springer.com/article/10.1007/s42484-023-00110-7" target="_blank" >https://link.springer.com/article/10.1007/s42484-023-00110-7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s42484-023-00110-7" target="_blank" >10.1007/s42484-023-00110-7</a>
Alternative languages
Result language
angličtina
Original language name
Multi-level quantum inspired metaheuristics for automatic clustering of hyperspectral images
Original language description
Hyperspectral images contain large spectral information with an abundance of redundancy and a curse of dimensionality. Due to the absence of prior knowledge or availability of ground-truth data, clustering of these images becomes a herculean task. Hence, unsupervised cluster detection methods are more beneficial for utilising hyperspectral images in real-life scenarios. In this paper, six multilevel quantum inspired metaheuristics are proposed viz., Qubit Genetic Algorithm, Qutrit Genetic Algorithm, Qubit Multi-exemplar Particle Swarm Optimization Algorithm, Qutrit Multi-exemplar Particle Swarm Optimization Algorithm, Qubit Artificial Humming Bird Algorithm, and Qutrit Artificial Humming Bird Algorithm, for determining the optimal number of clusters in hyperspectral images automatically. Binary and ternary quantum versions of the algorithms are developed to enhance their exploration and exploitation capabilities. Simple algorithms for implementing quantum rotation gates are developed to bring diversity in the population without resorting to look-up tables. One of the main features of quantum gates is that they are reversible in nature. This property has been utilized for implementing quantum disaster operations. The application of a dynamic number of exemplars also enhances the performance of the Multi-exemplar Particle Swarm Optimization Algorithm. The six proposed algorithms are compared to the classical Genetic Algorithm, Multi-exemplar Particle Swarm Optimization Algorithm, and Artificial Humming Bird Algorithm. All the nine algorithms are applied on three hyperspectral image datasets viz., Pavia University, Indian Pines, and Xuzhou HYSPEX datasets. Statistical tests like mean, standard deviation, Kruskal Wallis test, and Tukey's Post Hoc test are performed on all the nine algorithms to establish their efficiencies. Three cluster validity indices viz., Xie-Beni Index, Object-based Validation with densities, and Correlation Based Cluster Validity Index are used as the fitness function. The F, F', and Q scores are used to compare the clustered images. The proposed algorithms are found to perform better in most of the cases when compared to their classical counterparts. It is also observed that the qutrit versions of the algorithms are found to converge faster. They also provide the optimal number of clusters almost equivalent to the number of classes identified in the ground-truth image.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Quantum Machine Intelligence
ISSN
2524-4906
e-ISSN
2524-4914
Volume of the periodical
5
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
35
Pages from-to
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UT code for WoS article
000998864900001
EID of the result in the Scopus database
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