Resolution-enhanced Hyperspectral EnMAP data: CubeSat-based high resolution data fusion approach Article Swipe
Víctor Angulo
,
Kasper Johansen
,
J. L. Rodriguez
,
Omar A. Lopez
,
Jamal Elfarkh
,
Matthew F. McCabe
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu23-9489
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu23-9489
Hyperspectral (HS) images obtained from space are useful for monitoring different natural phenomena on regional to global scales. The Environmental Mapping and Analysis Program (EnMAP) is a satellite recently launched by Germany to monitor the environment and explore the capabilities of hyperspectral sensors in the 420 and 2450 nm range of the spectrum. However, the data captured by the EnMAP mission have a ground sampling distance (GSD) of 30 m. This limits the use of the data for some applications that require higher spatial resolution (<10 m). This study examines the potential for improving the resolution of hyperspectral data using high resolution multispectral (MS) data obtained by Cubesats. Specifically, this work uses the data captured by the PlanetScope constellation, which has more than 150 CubeSats in low Earth orbit, with a high spatial and temporal resolution. The approach adopted leverages (1) the spectral capability of the hyperspectral EnMAP sensor, with a bandwidth of 6.5 nm in the visible and near infrared (VNIR) range (420–1000 nm) and 10 nm in the SWIR range (900–2450 nm), and (2) the spatial capability of the multispectral PlanetScope data, with a GSD of 3 meters, to enable significant spatial improvements due to its high spatial resolution. The main components of this work include: (i) area of interest clipping (ii) data co-registration, (iii) HS-MS data fusion, and (iv) quality assessments using the Jointly Spectral and Spatial Quality Index (QNR). In this study, a 2 km x 2 km area of interest was selected in the Malaucene region of France, where six state-of-the-art HS-MS fusion methods were evaluated: (1) fast multi-band image fusion algorithm (FUSE), (2) coupled nonnegative matrix factorization (CNMF), (3) smoothing filtered-based intensity modulation (SFIMHS), (4) maximum a posteriori stochastic mixing model (MAPSMM), (5) Hyperspectral Superresolution (HySure), and (6) generalized laplacian pyramid hypersharpening (GLPHS). Quality assessments of the enhanced data showed that high spectral and spatial fidelity are maintained, with the best performing fusion method being FUSE with a QNR of 0.625 followed by the MAPSMM method with a QNR of 0.604. Overall, this study advocates the benefits associated with the fusion of hyperspectral and multispectral data to obtain enhanced EnMAP data at 3 m GSD.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-egu23-9489
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4322011523Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/egusphere-egu23-9489Digital Object Identifier
- Title
-
Resolution-enhanced Hyperspectral EnMAP data: CubeSat-based high resolution data fusion approachWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-02-25Full publication date if available
- Authors
-
Víctor Angulo, Kasper Johansen, J. L. Rodriguez, Omar A. Lopez, Jamal Elfarkh, Matthew F. McCabeList of authors in order
- Landing page
-
https://doi.org/10.5194/egusphere-egu23-9489Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5194/egusphere-egu23-9489Direct OA link when available
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Hyperspectral imaging, Remote sensing, VNIR, Multispectral image, Image resolution, Environmental science, Satellite, Sensor fusion, Earth observation, Computer science, Geography, Physics, Artificial intelligence, AstronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.QNR | 324, 334 |
| abstract_inverted_index.The | 18, 136, 201 |
| abstract_inverted_index.and | 21, 36, 46, 133, 158, 165, 174, 220, 228, 292, 309, 349 |
| abstract_inverted_index.are | 6, 312 |
| abstract_inverted_index.due | 195 |
| abstract_inverted_index.for | 8, 77, 92 |
| abstract_inverted_index.has | 120 |
| abstract_inverted_index.its | 197 |
| abstract_inverted_index.low | 126 |
| abstract_inverted_index.m). | 86 |
| abstract_inverted_index.nm) | 164 |
| abstract_inverted_index.six | 254 |
| abstract_inverted_index.the | 34, 38, 44, 51, 54, 58, 72, 75, 90, 94, 112, 116, 141, 145, 156, 169, 176, 180, 225, 248, 302, 315, 329, 341, 345 |
| abstract_inverted_index.use | 73 |
| abstract_inverted_index.was | 245 |
| abstract_inverted_index.(HS) | 1 |
| abstract_inverted_index.(MS) | 103 |
| abstract_inverted_index.(ii) | 213 |
| abstract_inverted_index.(iv) | 221 |
| abstract_inverted_index.2450 | 47 |
| abstract_inverted_index.FUSE | 321 |
| abstract_inverted_index.SWIR | 170 |
| abstract_inverted_index.This | 70, 87 |
| abstract_inverted_index.area | 209, 242 |
| abstract_inverted_index.best | 316 |
| abstract_inverted_index.data | 55, 76, 98, 104, 113, 214, 218, 304, 351, 356 |
| abstract_inverted_index.fast | 262 |
| abstract_inverted_index.from | 4 |
| abstract_inverted_index.have | 61 |
| abstract_inverted_index.high | 100, 131, 198, 307 |
| abstract_inverted_index.main | 202 |
| abstract_inverted_index.more | 121 |
| abstract_inverted_index.near | 159 |
| abstract_inverted_index.nm), | 173 |
| abstract_inverted_index.some | 78 |
| abstract_inverted_index.than | 122 |
| abstract_inverted_index.that | 80, 306 |
| abstract_inverted_index.this | 109, 205, 234, 338 |
| abstract_inverted_index.uses | 111 |
| abstract_inverted_index.were | 259 |
| abstract_inverted_index.with | 129, 149, 184, 314, 322, 332, 344 |
| abstract_inverted_index.work | 110, 206 |
| abstract_inverted_index.(GSD) | 66 |
| abstract_inverted_index.(iii) | 216 |
| abstract_inverted_index.0.625 | 326 |
| abstract_inverted_index.Earth | 127 |
| abstract_inverted_index.EnMAP | 59, 147, 355 |
| abstract_inverted_index.HS-MS | 217, 256 |
| abstract_inverted_index.Index | 231 |
| abstract_inverted_index.being | 320 |
| abstract_inverted_index.data, | 183 |
| abstract_inverted_index.image | 264 |
| abstract_inverted_index.model | 286 |
| abstract_inverted_index.range | 49, 162, 171 |
| abstract_inverted_index.space | 5 |
| abstract_inverted_index.study | 88, 339 |
| abstract_inverted_index.using | 99, 224 |
| abstract_inverted_index.where | 253 |
| abstract_inverted_index.which | 119 |
| abstract_inverted_index.(QNR). | 232 |
| abstract_inverted_index.(VNIR) | 161 |
| abstract_inverted_index.0.604. | 336 |
| abstract_inverted_index.MAPSMM | 330 |
| abstract_inverted_index.enable | 191 |
| abstract_inverted_index.fusion | 257, 265, 318, 346 |
| abstract_inverted_index.global | 16 |
| abstract_inverted_index.ground | 63 |
| abstract_inverted_index.higher | 82 |
| abstract_inverted_index.images | 2 |
| abstract_inverted_index.limits | 71 |
| abstract_inverted_index.matrix | 271 |
| abstract_inverted_index.method | 319, 331 |
| abstract_inverted_index.mixing | 285 |
| abstract_inverted_index.obtain | 353 |
| abstract_inverted_index.orbit, | 128 |
| abstract_inverted_index.region | 250 |
| abstract_inverted_index.showed | 305 |
| abstract_inverted_index.study, | 235 |
| abstract_inverted_index.useful | 7 |
| abstract_inverted_index.(<10 | 85 |
| abstract_inverted_index.(CNMF), | 273 |
| abstract_inverted_index.(EnMAP) | 24 |
| abstract_inverted_index.(FUSE), | 267 |
| abstract_inverted_index.France, | 252 |
| abstract_inverted_index.Germany | 31 |
| abstract_inverted_index.Jointly | 226 |
| abstract_inverted_index.Mapping | 20 |
| abstract_inverted_index.Program | 23 |
| abstract_inverted_index.Quality | 230, 299 |
| abstract_inverted_index.Spatial | 229 |
| abstract_inverted_index.adopted | 138 |
| abstract_inverted_index.coupled | 269 |
| abstract_inverted_index.explore | 37 |
| abstract_inverted_index.fusion, | 219 |
| abstract_inverted_index.maximum | 281 |
| abstract_inverted_index.meters, | 189 |
| abstract_inverted_index.methods | 258 |
| abstract_inverted_index.mission | 60 |
| abstract_inverted_index.monitor | 33 |
| abstract_inverted_index.natural | 11 |
| abstract_inverted_index.pyramid | 296 |
| abstract_inverted_index.quality | 222 |
| abstract_inverted_index.require | 81 |
| abstract_inverted_index.scales. | 17 |
| abstract_inverted_index.sensor, | 148 |
| abstract_inverted_index.sensors | 42 |
| abstract_inverted_index.spatial | 83, 132, 177, 193, 199, 310 |
| abstract_inverted_index.visible | 157 |
| abstract_inverted_index.(GLPHS). | 298 |
| abstract_inverted_index.Analysis | 22 |
| abstract_inverted_index.CubeSats | 124 |
| abstract_inverted_index.However, | 53 |
| abstract_inverted_index.Overall, | 337 |
| abstract_inverted_index.Spectral | 227 |
| abstract_inverted_index.approach | 137 |
| abstract_inverted_index.benefits | 342 |
| abstract_inverted_index.captured | 56, 114 |
| abstract_inverted_index.clipping | 212 |
| abstract_inverted_index.distance | 65 |
| abstract_inverted_index.enhanced | 303, 354 |
| abstract_inverted_index.examines | 89 |
| abstract_inverted_index.fidelity | 311 |
| abstract_inverted_index.followed | 327 |
| abstract_inverted_index.include: | 207 |
| abstract_inverted_index.infrared | 160 |
| abstract_inverted_index.interest | 211, 244 |
| abstract_inverted_index.launched | 29 |
| abstract_inverted_index.obtained | 3, 105 |
| abstract_inverted_index.recently | 28 |
| abstract_inverted_index.regional | 14 |
| abstract_inverted_index.sampling | 64 |
| abstract_inverted_index.selected | 246 |
| abstract_inverted_index.spectral | 142, 308 |
| abstract_inverted_index.temporal | 134 |
| abstract_inverted_index.(HySure), | 291 |
| abstract_inverted_index.(MAPSMM), | 287 |
| abstract_inverted_index.(SFIMHS), | 279 |
| abstract_inverted_index.Cubesats. | 107 |
| abstract_inverted_index.Malaucene | 249 |
| abstract_inverted_index.advocates | 340 |
| abstract_inverted_index.algorithm | 266 |
| abstract_inverted_index.bandwidth | 151 |
| abstract_inverted_index.different | 10 |
| abstract_inverted_index.improving | 93 |
| abstract_inverted_index.intensity | 277 |
| abstract_inverted_index.laplacian | 295 |
| abstract_inverted_index.leverages | 139 |
| abstract_inverted_index.phenomena | 12 |
| abstract_inverted_index.potential | 91 |
| abstract_inverted_index.satellite | 27 |
| abstract_inverted_index.smoothing | 275 |
| abstract_inverted_index.spectrum. | 52 |
| abstract_inverted_index.associated | 343 |
| abstract_inverted_index.capability | 143, 178 |
| abstract_inverted_index.components | 203 |
| abstract_inverted_index.evaluated: | 260 |
| abstract_inverted_index.modulation | 278 |
| abstract_inverted_index.monitoring | 9 |
| abstract_inverted_index.multi-band | 263 |
| abstract_inverted_index.performing | 317 |
| abstract_inverted_index.posteriori | 283 |
| abstract_inverted_index.resolution | 84, 95, 101 |
| abstract_inverted_index.stochastic | 284 |
| abstract_inverted_index.PlanetScope | 117, 182 |
| abstract_inverted_index.assessments | 223, 300 |
| abstract_inverted_index.environment | 35 |
| abstract_inverted_index.generalized | 294 |
| abstract_inverted_index.maintained, | 313 |
| abstract_inverted_index.nonnegative | 270 |
| abstract_inverted_index.resolution. | 135, 200 |
| abstract_inverted_index.significant | 192 |
| abstract_inverted_index.applications | 79 |
| abstract_inverted_index.capabilities | 39 |
| abstract_inverted_index.improvements | 194 |
| abstract_inverted_index.Environmental | 19 |
| abstract_inverted_index.Hyperspectral | 0, 289 |
| abstract_inverted_index.Specifically, | 108 |
| abstract_inverted_index.factorization | 272 |
| abstract_inverted_index.hyperspectral | 41, 97, 146, 348 |
| abstract_inverted_index.multispectral | 102, 181, 350 |
| abstract_inverted_index.GSD.&#160; | 360 |
| abstract_inverted_index.constellation, | 118 |
| abstract_inverted_index.filtered-based | 276 |
| abstract_inverted_index.Superresolution | 290 |
| abstract_inverted_index.hypersharpening | 297 |
| abstract_inverted_index.co-registration, | 215 |
| abstract_inverted_index.state-of-the-art | 255 |
| abstract_inverted_index.(420&#8211;1000 | 163 |
| abstract_inverted_index.(900&#8211;2450 | 172 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5089152837, https://openalex.org/A5024618773, https://openalex.org/A5108297965, https://openalex.org/A5075555774 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I71920554 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.02316747 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |