Neural network spectral relationship to improve an inherent optical properties data processing system for residual error correction Article Swipe
Jun Chen
,
J T Li
,
Xianqiang He
,
Junwu Tang
,
Delu Pan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1364/oe.498601
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1364/oe.498601
The residual error was a critical indicator to measure the data quality of ocean color products, which allows a user to decide the valuable envisioned application of these data. To effectively remove the residual errors from satellite remote sensing reflectance ( R rs ) using the inherent optical data processing system (IDAS), we expressed the residual error spectrum as an exponential plus linear function, and then we developed neural network models to derive the corresponding spectral slope coefficients from satellite R rs data. Coupled with the neural network models-based spectral relationship, the IDAS algorithm (IDAS nn ) was more effective than an invariant spectral relationship-based IDAS algorithm (IDAS cw ) in reducing the effects of residual errors in R rs on IOPs retrieval for our synthetic, field, and Chinese Ocean Color and Temperature Scanner (COCTS) data. Particularly, due to the improved spectral relationship of the residual errors, the IDAS nn algorithm provided more accurate and smoother spatiotemporal ocean color product than the IDAS cw algorithm for the open ocean. Furthermore, we could monitor the data quality with the IDAS nn algorithm, suggesting that the residual error was exceptionally large for COCTS images with low effective coverage. The product effective coverage should be rigorously controlled, or the residual error should be accurately corrected before temporal and spatial analysis of the COCTS data. Our results suggest that an accurate spectral relationship of residual errors is critical to determine how well the IDAS algorithm corrects for residual error.
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- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.1364/oe.498601
- OA Status
- gold
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388099066
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388099066Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1364/oe.498601Digital Object Identifier
- Title
-
Neural network spectral relationship to improve an inherent optical properties data processing system for residual error correctionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-10-30Full publication date if available
- Authors
-
Jun Chen, J T Li, Xianqiang He, Junwu Tang, Delu PanList of authors in order
- Landing page
-
https://doi.org/10.1364/oe.498601Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1364/oe.498601Direct OA link when available
- Concepts
-
Residual, Optics, Computer science, Artificial neural network, Data processing, Error detection and correction, Image processing, Artificial intelligence, Algorithm, Physics, Image (mathematics), Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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53Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.to | 7, 20, 71, 138, 234 |
| abstract_inverted_index.we | 52, 66, 170 |
| abstract_inverted_index.Our | 221 |
| abstract_inverted_index.The | 0, 196 |
| abstract_inverted_index.and | 64, 127, 131, 154, 214 |
| abstract_inverted_index.due | 137 |
| abstract_inverted_index.for | 123, 165, 189, 242 |
| abstract_inverted_index.how | 236 |
| abstract_inverted_index.low | 193 |
| abstract_inverted_index.our | 124 |
| abstract_inverted_index.the | 9, 22, 32, 45, 54, 73, 85, 91, 112, 139, 144, 147, 161, 166, 173, 177, 183, 205, 218, 238 |
| abstract_inverted_index.was | 3, 97, 186 |
| abstract_inverted_index.IDAS | 92, 105, 148, 162, 178, 239 |
| abstract_inverted_index.IOPs | 121 |
| abstract_inverted_index.data | 10, 48, 174 |
| abstract_inverted_index.from | 35, 78 |
| abstract_inverted_index.more | 98, 152 |
| abstract_inverted_index.open | 167 |
| abstract_inverted_index.plus | 61 |
| abstract_inverted_index.than | 100, 160 |
| abstract_inverted_index.that | 182, 224 |
| abstract_inverted_index.then | 65 |
| abstract_inverted_index.user | 19 |
| abstract_inverted_index.well | 237 |
| abstract_inverted_index.with | 84, 176, 192 |
| abstract_inverted_index.(IDAS | 94, 107 |
| abstract_inverted_index.COCTS | 190, 219 |
| abstract_inverted_index.Color | 130 |
| abstract_inverted_index.Ocean | 129 |
| abstract_inverted_index.color | 14, 158 |
| abstract_inverted_index.could | 171 |
| abstract_inverted_index.data. | 28, 82, 135, 220 |
| abstract_inverted_index.error | 2, 56, 185, 207 |
| abstract_inverted_index.large | 188 |
| abstract_inverted_index.ocean | 13, 157 |
| abstract_inverted_index.slope | 76 |
| abstract_inverted_index.these | 27 |
| abstract_inverted_index.using | 44 |
| abstract_inverted_index.which | 16 |
| abstract_inverted_index.allows | 17 |
| abstract_inverted_index.before | 212 |
| abstract_inverted_index.decide | 21 |
| abstract_inverted_index.derive | 72 |
| abstract_inverted_index.error. | 244 |
| abstract_inverted_index.errors | 34, 116, 231 |
| abstract_inverted_index.field, | 126 |
| abstract_inverted_index.images | 191 |
| abstract_inverted_index.linear | 62 |
| abstract_inverted_index.models | 70 |
| abstract_inverted_index.neural | 68, 86 |
| abstract_inverted_index.ocean. | 168 |
| abstract_inverted_index.remote | 37 |
| abstract_inverted_index.remove | 31 |
| abstract_inverted_index.should | 200, 208 |
| abstract_inverted_index.system | 50 |
| abstract_inverted_index.(COCTS) | 134 |
| abstract_inverted_index.(IDAS), | 51 |
| abstract_inverted_index.Chinese | 128 |
| abstract_inverted_index.Coupled | 83 |
| abstract_inverted_index.Scanner | 133 |
| abstract_inverted_index.effects | 113 |
| abstract_inverted_index.errors, | 146 |
| abstract_inverted_index.measure | 8 |
| abstract_inverted_index.monitor | 172 |
| abstract_inverted_index.network | 69, 87 |
| abstract_inverted_index.optical | 47 |
| abstract_inverted_index.product | 159, 197 |
| abstract_inverted_index.quality | 11, 175 |
| abstract_inverted_index.results | 222 |
| abstract_inverted_index.sensing | 38 |
| abstract_inverted_index.spatial | 215 |
| abstract_inverted_index.suggest | 223 |
| abstract_inverted_index.accurate | 153, 226 |
| abstract_inverted_index.analysis | 216 |
| abstract_inverted_index.corrects | 241 |
| abstract_inverted_index.coverage | 199 |
| abstract_inverted_index.critical | 5, 233 |
| abstract_inverted_index.improved | 140 |
| abstract_inverted_index.inherent | 46 |
| abstract_inverted_index.provided | 151 |
| abstract_inverted_index.reducing | 111 |
| abstract_inverted_index.residual | 1, 33, 55, 115, 145, 184, 206, 230, 243 |
| abstract_inverted_index.smoother | 155 |
| abstract_inverted_index.spectral | 75, 89, 103, 141, 227 |
| abstract_inverted_index.spectrum | 57 |
| abstract_inverted_index.temporal | 213 |
| abstract_inverted_index.valuable | 23 |
| abstract_inverted_index.algorithm | 93, 106, 150, 164, 240 |
| abstract_inverted_index.corrected | 211 |
| abstract_inverted_index.coverage. | 195 |
| abstract_inverted_index.determine | 235 |
| abstract_inverted_index.developed | 67 |
| abstract_inverted_index.effective | 99, 194, 198 |
| abstract_inverted_index.expressed | 53 |
| abstract_inverted_index.function, | 63 |
| abstract_inverted_index.indicator | 6 |
| abstract_inverted_index.invariant | 102 |
| abstract_inverted_index.products, | 15 |
| abstract_inverted_index.retrieval | 122 |
| abstract_inverted_index.satellite | 36, 79 |
| abstract_inverted_index.accurately | 210 |
| abstract_inverted_index.algorithm, | 180 |
| abstract_inverted_index.envisioned | 24 |
| abstract_inverted_index.processing | 49 |
| abstract_inverted_index.rigorously | 202 |
| abstract_inverted_index.suggesting | 181 |
| abstract_inverted_index.synthetic, | 125 |
| abstract_inverted_index.Temperature | 132 |
| abstract_inverted_index.application | 25 |
| abstract_inverted_index.controlled, | 203 |
| abstract_inverted_index.effectively | 30 |
| abstract_inverted_index.exponential | 60 |
| abstract_inverted_index.reflectance | 39 |
| abstract_inverted_index.Furthermore, | 169 |
| abstract_inverted_index.coefficients | 77 |
| abstract_inverted_index.models-based | 88 |
| abstract_inverted_index.relationship | 142, 228 |
| abstract_inverted_index.Particularly, | 136 |
| abstract_inverted_index.corresponding | 74 |
| abstract_inverted_index.exceptionally | 187 |
| abstract_inverted_index.relationship, | 90 |
| abstract_inverted_index.spatiotemporal | 156 |
| abstract_inverted_index.relationship-based | 104 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.15993004 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |