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Gray Wolf Optimizer for hyperspectral band selection
Applied Soft Computing, 2016Graphical abstractDisplay Omitted HighlightsWe propose a new approach for feature selection in hyperspectral image classification.The problem of band selection is reformulated as a combinatorial problem.We design a new objective function which takes into account two term, the classification error rate and the class separability distance.To optimize the
S.A. Medjahed +3 more
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Constrained Band Subset Selection for Hyperspectral Imagery
IEEE Geoscience and Remote Sensing Letters, 2017This letter extends the constrained band selection (CBS) technique to constrained band subset selection (CBSS) in a similar manner that constrained energy minimization has been extended to linearly constrained minimum variance. CBSS constrains multiple bands as a band subset as opposed to CBS constraining a single band as a singleton set.
Lin Wang +3 more
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Hyperspectral Band Selection by Virtual Dimensionality
2018Hyperspectral images are generally acquired by hundreds of contiguous spectral bands and provide a wealth of useful and crucial information for data analysis. However, on many occasions too many bands cause undesired effects, called curse of dimensionality.
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Hyperspectral band selection based on evolutionary optimization
2013 Ninth International Conference on Natural Computation (ICNC), 2013A hyperspectral image consists of a series of spectral bands which has brought great challenges to image processing and analysis. To alleviate the curse of dimensionality, band selection is therefore applied to the hyperspectral images. In this paper, a two-step method is proposed for band selection.
Qiannan Du +3 more
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Nature-Inspired Framework for Hyperspectral Band Selection
IEEE Transactions on Geoscience and Remote Sensing, 2014Although hyperspectral images acquired by on-board satellites provide information from a wide range of wavelengths in the spectrum, the obtained information is usually highly correlated. This paper proposes a novel framework to reduce the computation cost for large amounts of data based on the efficiency of the optimum-path forest (OPF) classifier and ...
Nakamura, Rodrigo Y. M. +5 more
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Supervised method for optimum hyperspectral band selection
SPIE Proceedings, 2013Much effort has been devoted to development of methods to reduce hyperspectral image dimensionality by locating and retaining data relevant for image interpretation while discarding that which is irrelevant. Irrelevance can result from an absence of information that could contribute to the classification, or from the presence of information that could ...
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A Tutorial on Terahertz-Band Localization for 6G Communication Systems
IEEE Communications Surveys and Tutorials, 2022Hui Chen, Hadi Sarieddeen, Tarig Ballal
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[Orthogonal projection divergence-based hyperspectral band selection].
Guang pu xue yu guang pu fen xi = Guang pu, 2015Due to the high data dimensionality of a hyperspectral image, dimensionality reduction algorithm has attracted much attention in hyperspectral image analysis. Band selection algorithm, which selects appropriate bands from the original set of spectral bands, can preserve original information from the data and is useful for image classification and ...
Hong-jun, Su +3 more
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Broadband convolutional processing using band-alignment-tunable heterostructures
Nature Electronics, 2022Pengfei Wang, Shi-Jun Liang, Xing Zhou
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