EBM3GP: A novel evolutionary bi-objective genetic programming for dimensionality reduction in classification of hyperspectral data
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- @Article{ZHOU:2023:infrared,
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author = "Zheng Zhou and Yu Yang and Gan Zhang and Libing Xu and
Mingqing Wang",
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title = "{EBM3GP:} A novel evolutionary bi-objective genetic
programming for dimensionality reduction in
classification of hyperspectral data",
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journal = "Infrared Physics \& Technology",
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volume = "129",
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pages = "104577",
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year = "2023",
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ISSN = "1350-4495",
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DOI = "doi:10.1016/j.infrared.2023.104577",
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URL = "https://www.sciencedirect.com/science/article/pii/S135044952300035X",
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keywords = "genetic algorithms, genetic programming,
Dimensionality reduction, Hyperspectral image, Low- and
high-level features, Shannon entropy, Cross entropy",
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abstract = "Dimensionality reduction (DR) is vital in
hyperspectral image (HSI) classification, and feature
extraction and band selection methods have been
demonstrated to be effective at accomplishing it.
However, both types of methods can only obtain
single-level features from HSI spectra, which suffers
from insufficient useful information and makes accurate
classification of the high dimensionality of HSI pixels
challenging. To overcome the shortcomings, this study
proposes a novel Evolutionary Bi-objective Genetic
Programming-based unsupervised DR approach named EBM3GP
for obtaining low-level features (bands) and high-level
features from raw HSI spectra simultaneously. In
EBM3GP, multi-dimensional trees are used to encode the
raw spectrum to low- and high-level features; two
mutually restrictive measures are applied to evaluate
the amount of information and the redundancy contained
in trees (evaluation does not use the HSI pixel's
label); multiple trees are optimized through population
evolution by combining three types of crossover
operators, two types of mutation operators and
nondominated sorting method; a Pareto optimal
individual is finally output and decoded as a DR
strategy. Then, this study applies Random Forest, least
squares Support Vector Machine, and Extreme Learning
Machine for classification to evaluate the efficacy of
the DR strategy. Based on three HSIs (including Indian
Pines, Salinas, and Pavia University datasets), EBM3GP
is demonstrated to outperform five popular DR methods
for HSI classification. Moreover, the EBM3GP is not
sensitive to data size and thus is available for DR of
small-size HSI datasets",
- }
Genetic Programming entries for
Zheng Zhou
Yu Yang
Gan Zhang
Libing Xu
Mingqing Wang
Citations