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WorldCat: 643243560
doi: 10.4316/AECE


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  4/2019 - 3

Incorporated Decision-maker-based Multiobjective Band Selection for Pixel Classification of Hyperspectral Images

SAQUI, D. See more information about SAQUI, D. on SCOPUS See more information about SAQUI, D. on IEEExplore See more information about SAQUI, D. on Web of Science, SAITO, J. H. See more information about  SAITO, J. H. on SCOPUS See more information about  SAITO, J. H. on SCOPUS See more information about SAITO, J. H. on Web of Science, De LIMA, D. C. See more information about  De LIMA, D. C. on SCOPUS See more information about  De LIMA, D. C. on SCOPUS See more information about De LIMA, D. C. on Web of Science, Del Val CURA, L. M. See more information about  Del Val CURA, L. M. on SCOPUS See more information about  Del Val CURA, L. M. on SCOPUS See more information about Del Val CURA, L. M. on Web of Science, ATAKY, S. T. M. See more information about ATAKY, S. T. M. on SCOPUS See more information about ATAKY, S. T. M. on SCOPUS See more information about ATAKY, S. T. M. on Web of Science
 
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Author keywords
remote sensing, hyperspectral imaging, image segmentation, image classification, evolutionary computation

References keywords
hyperspectral(26), remote(21), selection(20), sensing(17), band(14), classification(12), geoscience(10), image(8), feature(7), tgrs(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-11-30
Volume 19, Issue 4, Year 2019, On page(s): 21 - 28
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.04003
Web of Science Accession Number: 000500274700003
SCOPUS ID: 85077276122

Abstract
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Hyperspectral images (HIs) are characterized by a higher spectral resolution than other images and have applications in various fields, to wit, medicine, agriculture, mining, among others. Segmentation can be obtained from the pixel classification and it is a powerful tool for object identification. Notwithstanding, the problems of the curse of dimensionality and the demand for computational resources occur due to the number of bands. Techniques that reduce dimensionality, such as genetic algorithms, are promising, but they cannot assure a balance between conflicting objectives such as improving classification and reducing the number of bands. Multiobjective band selection can be applied to search for tradeoff solutions that have this balance. Therefore, in this manuscript, we propose a novel method called Incorporated Decision-Marker-based multiobjective band selection (IDMMoBS) that tries to find tradeoff solutions using spectral and spatial information. In the experiments, the IDMMoBS reduced the number of bands between 85.4 and 85.8 percent of the total and it outperformed the majority of other methods compared in this criterion. For the pixel classification, the IDMMoBS presented better results than all compared cases taking into account all evaluated metrics using SVM classifier. Accordingly, the IDMMoBS is suitable for band selection.


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References Weight

Web of Science® Citations for all references: 6,386 TCR
SCOPUS® Citations for all references: 9,656 TCR

Web of Science® Average Citations per reference: 164 ACR
SCOPUS® Average Citations per reference: 248 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-07-18 18:38 in 224 seconds.




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