Spectral Constrained Generative Adversarial Network for Hyperspectral Compression Article Swipe
Yuanyuan Guo
,
Weizhong Li
,
Qi Peng
,
Li-fen Tu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3599996
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3599996
Lossy compression exhibits remarkable capabilities in handling large volumes of data. However, information loss can affect spectral characteristics and spatial information to various degrees during hyperspectral compression. Therefore, it is essential to restrict the range of spectral changes, as each spectral curve corresponds to a distinct semantic context. In this article, we propose a spectral-constrained generative adversarial network (SCGAN) for hyperspectral compression. Specifically, SCGAN integrates compression and classification tasks within a unified framework. During generative adversarial learning, SCGAN uses a classification map to guide the generation of global spectral information. To deal with different hyperspectral images (HSIs) in one model, a three-stage training strategy is leveraged. Experiments conducted on three public HSI datasets illustrate that the proposed SCGAN effectively narrows the semantic gap. For instance, the average overall classification accuracy of SCGAN on Pavia University is above 0.9, which is the closest to the classification accuracy achieved with the original HSIs, even at a low bitrate of 0.05 bits per pixel per band.
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https://openalex.org/W4413319044Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/jstars.2025.3599996Digital Object Identifier
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Spectral Constrained Generative Adversarial Network for Hyperspectral CompressionWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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Yuanyuan Guo, Weizhong Li, Qi Peng, Li-fen TuList of authors in order
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https://doi.org/10.1109/jstars.2025.3599996Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/jstars.2025.3599996Direct OA link when available
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Hyperspectral imaging, Adversarial system, Computer science, Generative grammar, Data compression, Compression (physics), Generative adversarial network, Artificial intelligence, Deep learning, Materials science, Composite materialTop concepts (fields/topics) attached by OpenAlex
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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.on | 108, 132 |
| abstract_inverted_index.to | 21, 31, 43, 82, 142 |
| abstract_inverted_index.we | 51 |
| abstract_inverted_index.For | 123 |
| abstract_inverted_index.HSI | 111 |
| abstract_inverted_index.and | 18, 66 |
| abstract_inverted_index.can | 14 |
| abstract_inverted_index.for | 59 |
| abstract_inverted_index.low | 154 |
| abstract_inverted_index.map | 81 |
| abstract_inverted_index.one | 98 |
| abstract_inverted_index.per | 159, 161 |
| abstract_inverted_index.the | 33, 84, 115, 120, 125, 140, 143, 148 |
| abstract_inverted_index.0.05 | 157 |
| abstract_inverted_index.0.9, | 137 |
| abstract_inverted_index.bits | 158 |
| abstract_inverted_index.deal | 91 |
| abstract_inverted_index.each | 39 |
| abstract_inverted_index.even | 151 |
| abstract_inverted_index.gap. | 122 |
| abstract_inverted_index.loss | 13 |
| abstract_inverted_index.that | 114 |
| abstract_inverted_index.this | 49 |
| abstract_inverted_index.uses | 78 |
| abstract_inverted_index.with | 92, 147 |
| abstract_inverted_index.HSIs, | 150 |
| abstract_inverted_index.Lossy | 0 |
| abstract_inverted_index.Pavia | 133 |
| abstract_inverted_index.SCGAN | 63, 77, 117, 131 |
| abstract_inverted_index.above | 136 |
| abstract_inverted_index.band. | 162 |
| abstract_inverted_index.curve | 41 |
| abstract_inverted_index.data. | 10 |
| abstract_inverted_index.guide | 83 |
| abstract_inverted_index.large | 7 |
| abstract_inverted_index.pixel | 160 |
| abstract_inverted_index.range | 34 |
| abstract_inverted_index.tasks | 68 |
| abstract_inverted_index.three | 109 |
| abstract_inverted_index.which | 138 |
| abstract_inverted_index.(HSIs) | 96 |
| abstract_inverted_index.During | 73 |
| abstract_inverted_index.affect | 15 |
| abstract_inverted_index.during | 24 |
| abstract_inverted_index.global | 87 |
| abstract_inverted_index.images | 95 |
| abstract_inverted_index.model, | 99 |
| abstract_inverted_index.public | 110 |
| abstract_inverted_index.within | 69 |
| abstract_inverted_index.(SCGAN) | 58 |
| abstract_inverted_index.average | 126 |
| abstract_inverted_index.bitrate | 155 |
| abstract_inverted_index.closest | 141 |
| abstract_inverted_index.degrees | 23 |
| abstract_inverted_index.narrows | 119 |
| abstract_inverted_index.network | 57 |
| abstract_inverted_index.overall | 127 |
| abstract_inverted_index.propose | 52 |
| abstract_inverted_index.spatial | 19 |
| abstract_inverted_index.unified | 71 |
| abstract_inverted_index.various | 22 |
| abstract_inverted_index.volumes | 8 |
| abstract_inverted_index.However, | 11 |
| abstract_inverted_index.accuracy | 129, 145 |
| abstract_inverted_index.achieved | 146 |
| abstract_inverted_index.article, | 50 |
| abstract_inverted_index.changes, | 37 |
| abstract_inverted_index.context. | 47 |
| abstract_inverted_index.datasets | 112 |
| abstract_inverted_index.distinct | 45 |
| abstract_inverted_index.exhibits | 2 |
| abstract_inverted_index.handling | 6 |
| abstract_inverted_index.original | 149 |
| abstract_inverted_index.proposed | 116 |
| abstract_inverted_index.restrict | 32 |
| abstract_inverted_index.semantic | 46, 121 |
| abstract_inverted_index.spectral | 16, 36, 40, 88 |
| abstract_inverted_index.strategy | 103 |
| abstract_inverted_index.training | 102 |
| abstract_inverted_index.conducted | 107 |
| abstract_inverted_index.different | 93 |
| abstract_inverted_index.essential | 30 |
| abstract_inverted_index.instance, | 124 |
| abstract_inverted_index.learning, | 76 |
| abstract_inverted_index.Therefore, | 27 |
| abstract_inverted_index.University | 134 |
| abstract_inverted_index.framework. | 72 |
| abstract_inverted_index.generation | 85 |
| abstract_inverted_index.generative | 55, 74 |
| abstract_inverted_index.illustrate | 113 |
| abstract_inverted_index.integrates | 64 |
| abstract_inverted_index.leveraged. | 105 |
| abstract_inverted_index.remarkable | 3 |
| abstract_inverted_index.Experiments | 106 |
| abstract_inverted_index.adversarial | 56, 75 |
| abstract_inverted_index.compression | 1, 65 |
| abstract_inverted_index.corresponds | 42 |
| abstract_inverted_index.effectively | 118 |
| abstract_inverted_index.information | 12, 20 |
| abstract_inverted_index.three-stage | 101 |
| abstract_inverted_index.capabilities | 4 |
| abstract_inverted_index.compression. | 26, 61 |
| abstract_inverted_index.information. | 89 |
| abstract_inverted_index.Specifically, | 62 |
| abstract_inverted_index.hyperspectral | 25, 60, 94 |
| abstract_inverted_index.classification | 67, 80, 128, 144 |
| abstract_inverted_index.characteristics | 17 |
| abstract_inverted_index.spectral-constrained | 54 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.37259969 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |