Evaluation of colour space effect on estimation accuracy of hyperspectral image by dimension extension based on RGB image Article Swipe
Ryoji Sato
,
Yuri Hamada
,
Takashi Kaburagi
,
Yosuke Kurihara
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1080/18824889.2022.2048532
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1080/18824889.2022.2048532
Recently, the utilization of hyperspectral images containing several hundred wavelength information has been increasing in various fields. If a hyperspectral image can be estimated from a low-cost RGB image that has only R, G, and B wavelength information without using a hyperspectral camera, it would be useful in various fields. Herein, we propose a hyperspectral image estimation method based on RGB images, wherein RGB components and YUV colour space information calculated from the RGB are applied to a neural network for tuning, and the hyperspectral image is estimated by inputting the output from the tuning neural network to a decoding function of the trained autoencoder. To evaluate the estimation accuracy of hyperspectral images based on differences in the combination of RGB and colour space models, we conducted validity experiments for the estimation of hyperspectral images in three scenarios with different colour spaces: RGB and YUV, RGB and HSV, only RGB. The results showed that the scenario with RGB and YUV colour space exhibited the highest estimation accuracy of 0.913 by averaging all similarities for wavelength among the three scenarios; thus, the validity of the proposed method as an estimation method for hyperspectral images was verified.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/18824889.2022.2048532
- https://www.tandfonline.com/doi/pdf/10.1080/18824889.2022.2048532?needAccess=true
- OA Status
- gold
- Cited By
- 2
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221006137
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https://openalex.org/W4221006137Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/18824889.2022.2048532Digital Object Identifier
- Title
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Evaluation of colour space effect on estimation accuracy of hyperspectral image by dimension extension based on RGB imageWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-03-31Full publication date if available
- Authors
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Ryoji Sato, Yuri Hamada, Takashi Kaburagi, Yosuke KuriharaList of authors in order
- Landing page
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https://doi.org/10.1080/18824889.2022.2048532Publisher landing page
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https://www.tandfonline.com/doi/pdf/10.1080/18824889.2022.2048532?needAccess=trueDirect link to full text PDF
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goldOpen access status per OpenAlex
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https://www.tandfonline.com/doi/pdf/10.1080/18824889.2022.2048532?needAccess=trueDirect OA link when available
- Concepts
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Hyperspectral imaging, RGB color model, Artificial intelligence, Computer vision, Computer science, Mathematics, Pattern recognition (psychology), Artificial neural networkTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2024: 1, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.based | 58, 113 |
| abstract_inverted_index.image | 20, 28, 55, 85 |
| abstract_inverted_index.space | 68, 123, 161 |
| abstract_inverted_index.three | 136, 177 |
| abstract_inverted_index.thus, | 179 |
| abstract_inverted_index.using | 39 |
| abstract_inverted_index.would | 44 |
| abstract_inverted_index.colour | 67, 122, 140, 160 |
| abstract_inverted_index.images | 5, 112, 134, 192 |
| abstract_inverted_index.method | 57, 185, 189 |
| abstract_inverted_index.neural | 78, 95 |
| abstract_inverted_index.output | 91 |
| abstract_inverted_index.showed | 152 |
| abstract_inverted_index.tuning | 94 |
| abstract_inverted_index.useful | 46 |
| abstract_inverted_index.Herein, | 50 |
| abstract_inverted_index.applied | 75 |
| abstract_inverted_index.camera, | 42 |
| abstract_inverted_index.fields. | 16, 49 |
| abstract_inverted_index.highest | 164 |
| abstract_inverted_index.hundred | 8 |
| abstract_inverted_index.images, | 61 |
| abstract_inverted_index.models, | 124 |
| abstract_inverted_index.network | 79, 96 |
| abstract_inverted_index.propose | 52 |
| abstract_inverted_index.results | 151 |
| abstract_inverted_index.several | 7 |
| abstract_inverted_index.spaces: | 141 |
| abstract_inverted_index.trained | 103 |
| abstract_inverted_index.tuning, | 81 |
| abstract_inverted_index.various | 15, 48 |
| abstract_inverted_index.wherein | 62 |
| abstract_inverted_index.without | 38 |
| abstract_inverted_index.accuracy | 109, 166 |
| abstract_inverted_index.decoding | 99 |
| abstract_inverted_index.evaluate | 106 |
| abstract_inverted_index.function | 100 |
| abstract_inverted_index.low-cost | 26 |
| abstract_inverted_index.proposed | 184 |
| abstract_inverted_index.scenario | 155 |
| abstract_inverted_index.validity | 127, 181 |
| abstract_inverted_index.Recently, | 0 |
| abstract_inverted_index.averaging | 170 |
| abstract_inverted_index.conducted | 126 |
| abstract_inverted_index.different | 139 |
| abstract_inverted_index.estimated | 23, 87 |
| abstract_inverted_index.exhibited | 162 |
| abstract_inverted_index.inputting | 89 |
| abstract_inverted_index.scenarios | 137 |
| abstract_inverted_index.verified. | 194 |
| abstract_inverted_index.calculated | 70 |
| abstract_inverted_index.components | 64 |
| abstract_inverted_index.containing | 6 |
| abstract_inverted_index.estimation | 56, 108, 131, 165, 188 |
| abstract_inverted_index.increasing | 13 |
| abstract_inverted_index.scenarios; | 178 |
| abstract_inverted_index.wavelength | 9, 36, 174 |
| abstract_inverted_index.combination | 118 |
| abstract_inverted_index.differences | 115 |
| abstract_inverted_index.experiments | 128 |
| abstract_inverted_index.information | 10, 37, 69 |
| abstract_inverted_index.utilization | 2 |
| abstract_inverted_index.autoencoder. | 104 |
| abstract_inverted_index.similarities | 172 |
| abstract_inverted_index.hyperspectral | 4, 19, 41, 54, 84, 111, 133, 191 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5108206972 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I131231118 |
| citation_normalized_percentile.value | 0.53712025 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |