Enhanced Wild Horse Optimizer with Cauchy Mutation and Dynamic Random Search for Hyperspectral Image Band Selection Article Swipe
Tao Chen
,
Yue Sun
,
Huayue Chen
,
Wu Deng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13101930
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13101930
The high dimensionality of hyperspectral images (HSIs) brings significant redundancy to data processing. Band selection (BS) is one of the most commonly used dimensionality reduction (DR) techniques, which eliminates redundant information between bands while retaining a subset of bands with a high information content and low noise. The wild horse optimizer (WHO) is a novel metaheuristic algorithm widely used for its efficient search performance, yet it tends to become trapped in local optima during later iterations. To address these issues, an enhanced wild horse optimizer (IBSWHO) is proposed for HSI band selection in this paper. IBSWHO utilizes Sobol sequences to initialize the population, thereby increasing population diversity. It incorporates Cauchy mutation to perturb the population with a certain probability, enhancing the global search capability and avoiding local optima. Additionally, dynamic random search techniques are introduced to improve the algorithm search efficiency and expand the search space. The convergence of IBSWHO is verified on commonly used nonlinear test functions and compared with state-of-the-art optimization algorithms. Finally, experiments on three classic HSI datasets are conducted for HSI classification. The experimental results demonstrate that the band subset selected by IBSWHO achieves the best classification accuracy compared to conventional and state-of-the-art band selection methods, confirming the superiority of the proposed BS method.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics13101930
- https://www.mdpi.com/2079-9292/13/10/1930/pdf?version=1715757706
- OA Status
- gold
- Cited By
- 8
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396930185
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396930185Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/electronics13101930Digital Object Identifier
- Title
-
Enhanced Wild Horse Optimizer with Cauchy Mutation and Dynamic Random Search for Hyperspectral Image Band SelectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-15Full publication date if available
- Authors
-
Tao Chen, Yue Sun, Huayue Chen, Wu DengList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics13101930Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/13/10/1930/pdf?version=1715757706Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2079-9292/13/10/1930/pdf?version=1715757706Direct OA link when available
- Concepts
-
Hyperspectral imaging, Selection (genetic algorithm), Mutation, Artificial intelligence, Image (mathematics), Computer science, Pattern recognition (psychology), Computer vision, Mathematics, Mathematical optimization, Biology, Genetics, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 4Per-year citation counts (last 5 years)
- References (count)
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58Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4391305849, https://openalex.org/W4404057074, https://openalex.org/W3153282519, https://openalex.org/W4391804634, https://openalex.org/W4383227077, https://openalex.org/W2177443438, https://openalex.org/W3120808882, https://openalex.org/W2316226477, https://openalex.org/W3046819794, https://openalex.org/W6839631182, https://openalex.org/W2978620371, https://openalex.org/W4283695078, https://openalex.org/W4292854290, https://openalex.org/W6848681095, https://openalex.org/W4385146139, https://openalex.org/W4386412328, https://openalex.org/W6842219918, https://openalex.org/W4397026515, https://openalex.org/W4394769928, https://openalex.org/W4388450845, https://openalex.org/W4390906206, https://openalex.org/W4390858576, https://openalex.org/W4386783335, https://openalex.org/W2005871861, https://openalex.org/W2177188361, https://openalex.org/W6775895450, https://openalex.org/W4387745472, https://openalex.org/W4307722364, https://openalex.org/W4392130001, https://openalex.org/W3212797097, https://openalex.org/W4392939608, https://openalex.org/W4383553809, https://openalex.org/W4390044085, https://openalex.org/W4396593371, https://openalex.org/W4386825433, https://openalex.org/W2042253843, https://openalex.org/W3083112738, https://openalex.org/W3083328146, https://openalex.org/W4225309412, https://openalex.org/W3046319801, https://openalex.org/W3173455696, https://openalex.org/W3199389790, https://openalex.org/W4295127880, https://openalex.org/W3212808017, https://openalex.org/W4312834201, https://openalex.org/W4224288469, https://openalex.org/W2151554678, https://openalex.org/W3126446412, https://openalex.org/W2908286879, https://openalex.org/W2085545731, https://openalex.org/W2056379908, https://openalex.org/W4310286287, https://openalex.org/W4385337173, https://openalex.org/W2168481151, https://openalex.org/W4293812006, https://openalex.org/W4285111340, https://openalex.org/W3014880914, https://openalex.org/W4312992590 |
| referenced_works_count | 58 |
| abstract_inverted_index.a | 35, 40, 53, 116 |
| abstract_inverted_index.BS | 206 |
| abstract_inverted_index.It | 107 |
| abstract_inverted_index.To | 76 |
| abstract_inverted_index.an | 80 |
| abstract_inverted_index.by | 185 |
| abstract_inverted_index.in | 70, 92 |
| abstract_inverted_index.is | 16, 52, 86, 150 |
| abstract_inverted_index.it | 65 |
| abstract_inverted_index.of | 3, 18, 37, 148, 203 |
| abstract_inverted_index.on | 152, 166 |
| abstract_inverted_index.to | 10, 67, 99, 111, 135, 193 |
| abstract_inverted_index.HSI | 89, 169, 174 |
| abstract_inverted_index.The | 0, 47, 146, 176 |
| abstract_inverted_index.and | 44, 124, 141, 158, 195 |
| abstract_inverted_index.are | 133, 171 |
| abstract_inverted_index.for | 59, 88, 173 |
| abstract_inverted_index.its | 60 |
| abstract_inverted_index.low | 45 |
| abstract_inverted_index.one | 17 |
| abstract_inverted_index.the | 19, 101, 113, 120, 137, 143, 181, 188, 201, 204 |
| abstract_inverted_index.yet | 64 |
| abstract_inverted_index.(BS) | 15 |
| abstract_inverted_index.(DR) | 25 |
| abstract_inverted_index.Band | 13 |
| abstract_inverted_index.band | 90, 182, 197 |
| abstract_inverted_index.best | 189 |
| abstract_inverted_index.data | 11 |
| abstract_inverted_index.high | 1, 41 |
| abstract_inverted_index.most | 20 |
| abstract_inverted_index.test | 156 |
| abstract_inverted_index.that | 180 |
| abstract_inverted_index.this | 93 |
| abstract_inverted_index.used | 22, 58, 154 |
| abstract_inverted_index.wild | 48, 82 |
| abstract_inverted_index.with | 39, 115, 160 |
| abstract_inverted_index.(WHO) | 51 |
| abstract_inverted_index.Sobol | 97 |
| abstract_inverted_index.bands | 32, 38 |
| abstract_inverted_index.horse | 49, 83 |
| abstract_inverted_index.later | 74 |
| abstract_inverted_index.local | 71, 126 |
| abstract_inverted_index.novel | 54 |
| abstract_inverted_index.tends | 66 |
| abstract_inverted_index.these | 78 |
| abstract_inverted_index.three | 167 |
| abstract_inverted_index.which | 27 |
| abstract_inverted_index.while | 33 |
| abstract_inverted_index.(HSIs) | 6 |
| abstract_inverted_index.Cauchy | 109 |
| abstract_inverted_index.IBSWHO | 95, 149, 186 |
| abstract_inverted_index.become | 68 |
| abstract_inverted_index.brings | 7 |
| abstract_inverted_index.during | 73 |
| abstract_inverted_index.expand | 142 |
| abstract_inverted_index.global | 121 |
| abstract_inverted_index.images | 5 |
| abstract_inverted_index.noise. | 46 |
| abstract_inverted_index.optima | 72 |
| abstract_inverted_index.paper. | 94 |
| abstract_inverted_index.random | 130 |
| abstract_inverted_index.search | 62, 122, 131, 139, 144 |
| abstract_inverted_index.space. | 145 |
| abstract_inverted_index.subset | 36, 183 |
| abstract_inverted_index.widely | 57 |
| abstract_inverted_index.address | 77 |
| abstract_inverted_index.between | 31 |
| abstract_inverted_index.certain | 117 |
| abstract_inverted_index.classic | 168 |
| abstract_inverted_index.content | 43 |
| abstract_inverted_index.dynamic | 129 |
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| abstract_inverted_index.issues, | 79 |
| abstract_inverted_index.method. | 207 |
| abstract_inverted_index.optima. | 127 |
| abstract_inverted_index.perturb | 112 |
| abstract_inverted_index.results | 178 |
| abstract_inverted_index.thereby | 103 |
| abstract_inverted_index.trapped | 69 |
| abstract_inverted_index.(IBSWHO) | 85 |
| abstract_inverted_index.Finally, | 164 |
| abstract_inverted_index.accuracy | 191 |
| abstract_inverted_index.achieves | 187 |
| abstract_inverted_index.avoiding | 125 |
| abstract_inverted_index.commonly | 21, 153 |
| abstract_inverted_index.compared | 159, 192 |
| abstract_inverted_index.datasets | 170 |
| abstract_inverted_index.enhanced | 81 |
| abstract_inverted_index.methods, | 199 |
| abstract_inverted_index.mutation | 110 |
| abstract_inverted_index.proposed | 87, 205 |
| abstract_inverted_index.selected | 184 |
| abstract_inverted_index.utilizes | 96 |
| abstract_inverted_index.verified | 151 |
| abstract_inverted_index.algorithm | 56, 138 |
| abstract_inverted_index.conducted | 172 |
| abstract_inverted_index.efficient | 61 |
| abstract_inverted_index.enhancing | 119 |
| abstract_inverted_index.functions | 157 |
| abstract_inverted_index.nonlinear | 155 |
| abstract_inverted_index.optimizer | 50, 84 |
| abstract_inverted_index.reduction | 24 |
| abstract_inverted_index.redundant | 29 |
| abstract_inverted_index.retaining | 34 |
| abstract_inverted_index.selection | 14, 91, 198 |
| abstract_inverted_index.sequences | 98 |
| abstract_inverted_index.capability | 123 |
| abstract_inverted_index.confirming | 200 |
| abstract_inverted_index.diversity. | 106 |
| abstract_inverted_index.efficiency | 140 |
| abstract_inverted_index.eliminates | 28 |
| abstract_inverted_index.increasing | 104 |
| abstract_inverted_index.initialize | 100 |
| abstract_inverted_index.introduced | 134 |
| abstract_inverted_index.population | 105, 114 |
| abstract_inverted_index.redundancy | 9 |
| abstract_inverted_index.techniques | 132 |
| abstract_inverted_index.algorithms. | 163 |
| abstract_inverted_index.convergence | 147 |
| abstract_inverted_index.demonstrate | 179 |
| abstract_inverted_index.experiments | 165 |
| abstract_inverted_index.information | 30, 42 |
| abstract_inverted_index.iterations. | 75 |
| abstract_inverted_index.population, | 102 |
| abstract_inverted_index.processing. | 12 |
| abstract_inverted_index.significant | 8 |
| abstract_inverted_index.superiority | 202 |
| abstract_inverted_index.techniques, | 26 |
| abstract_inverted_index.conventional | 194 |
| abstract_inverted_index.experimental | 177 |
| abstract_inverted_index.incorporates | 108 |
| abstract_inverted_index.optimization | 162 |
| abstract_inverted_index.performance, | 63 |
| abstract_inverted_index.probability, | 118 |
| abstract_inverted_index.Additionally, | 128 |
| abstract_inverted_index.hyperspectral | 4 |
| abstract_inverted_index.metaheuristic | 55 |
| abstract_inverted_index.classification | 190 |
| abstract_inverted_index.dimensionality | 2, 23 |
| abstract_inverted_index.classification. | 175 |
| abstract_inverted_index.state-of-the-art | 161, 196 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5100764224, https://openalex.org/A5103039365 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I16351329, https://openalex.org/I28813325, https://openalex.org/I4800084 |
| citation_normalized_percentile.value | 0.93828594 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |