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Incorporated Decision-maker-based Multiobjective Band Selection for Pixel Classification of Hyperspectral ImagesSAQUI, D. , SAITO, J. H. , De LIMA, D. C. , Del Val CURA, L. M. , ATAKY, S. T. M.
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remote sensing, hyperspectral imaging, image segmentation, image classification, evolutionary computation
hyperspectral(26), remote(21), selection(20), sensing(17), band(14), classification(12), geoscience(10), image(8), feature(7), tgrs(6)
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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
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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 Multiobjective Optimization-Based Hyperspectral Band Selection for Target Detection, Song, Meiping, Liu, Shihui, Xu, Dayong, Yu, Haoyang, IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, Issue , 2022.
Digital Object Identifier: 10.1109/TGRS.2022.3176856 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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