Hyperspectral image classification using meta-heuristics and artificial neural network.
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| Title: | Hyperspectral image classification using meta-heuristics and artificial neural network. |
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| Authors: | Dhingra, Sakshi1 (AUTHOR) sakshi24.dhingra@gmail.com, Kumar, Dharminder1 (AUTHOR) dr_dk_kumar_02@yahoo.com |
| Source: | Journal of Information & Optimization Sciences. Dec2022, Vol. 43 Issue 8, p2167-2179. 13p. |
| Subjects: | Feature selection, Metaheuristic algorithms, Global optimization, Spectral imaging, Genetic algorithms, Data structures, Classification |
| Abstract: | Hyperspectral images usually comprise several continuous spectral bands that represent the category of similar objects or material within the captured scene. These high-dimensional data structures have a high level of correlation and possess unique information that can be used for precise image classification. The precise selection of useful features from these high dimensional band information is very important to reduce the challenge of hyper spectral image classification approaches. Nowadays, metaheuristic algorithms are immensely utilized as a promising tool for hyperspectral image classification. In the present research work, hyperspectral images are classified with the various combinations of meta-heuristic approaches and the neural network including the mostly used Cuckoo Search (CS) optimization algorithm to resolve the global optimization search problems considering the improvement needed in image classification. Further, the strength of CS is improved using the integration of the Genetic Algorithm (GA) fitness function within the CS. The feature selection is performed by the hybrid CS and GA algorithm and the optimized features are then fed to ANN for training and classification. The paper has shown a comparative analysis of various meta heuristics techniques with ANN on parameters like kappa coefficient, Class accuracy and overall Accuracy and the designed algorithms are tested on the Indian Pines dataset. The proposed CS and GA with ANN outperformed the two already existing works with an overall average accuracy of 97.30% and a kappa coefficient of 0.9760. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Information & Optimization Sciences is the property of Taru Publications and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 160870852 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hyperspectral image classification using meta-heuristics and artificial neural network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dhingra%2C+Sakshi%22">Dhingra, Sakshi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sakshi24.dhingra@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Dharminder%22">Kumar, Dharminder</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dr_dk_kumar_02@yahoo.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Information+%26+Optimization+Sciences%22">Journal of Information & Optimization Sciences</searchLink>. Dec2022, Vol. 43 Issue 8, p2167-2179. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Global+optimization%22">Global optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Spectral+imaging%22">Spectral imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Hyperspectral images usually comprise several continuous spectral bands that represent the category of similar objects or material within the captured scene. These high-dimensional data structures have a high level of correlation and possess unique information that can be used for precise image classification. The precise selection of useful features from these high dimensional band information is very important to reduce the challenge of hyper spectral image classification approaches. Nowadays, metaheuristic algorithms are immensely utilized as a promising tool for hyperspectral image classification. In the present research work, hyperspectral images are classified with the various combinations of meta-heuristic approaches and the neural network including the mostly used Cuckoo Search (CS) optimization algorithm to resolve the global optimization search problems considering the improvement needed in image classification. Further, the strength of CS is improved using the integration of the Genetic Algorithm (GA) fitness function within the CS. The feature selection is performed by the hybrid CS and GA algorithm and the optimized features are then fed to ANN for training and classification. The paper has shown a comparative analysis of various meta heuristics techniques with ANN on parameters like kappa coefficient, Class accuracy and overall Accuracy and the designed algorithms are tested on the Indian Pines dataset. The proposed CS and GA with ANN outperformed the two already existing works with an overall average accuracy of 97.30% and a kappa coefficient of 0.9760. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Information & Optimization Sciences is the property of Taru Publications and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/02522667.2022.2133222 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 2167 Subjects: – SubjectFull: Feature selection Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Global optimization Type: general – SubjectFull: Spectral imaging Type: general – SubjectFull: Genetic algorithms Type: general – SubjectFull: Data structures Type: general – SubjectFull: Classification Type: general Titles: – TitleFull: Hyperspectral image classification using meta-heuristics and artificial neural network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dhingra, Sakshi – PersonEntity: Name: NameFull: Kumar, Dharminder IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 02522667 Numbering: – Type: volume Value: 43 – Type: issue Value: 8 Titles: – TitleFull: Journal of Information & Optimization Sciences Type: main |
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