CSCN: A Cross-Scan Semantic Cluster Network with Scene Coupling Attention for Remote Sensing Segmentation Article Swipe
Lei Zhang
,
Xing Xing
,
Changfeng Jing
,
Min Jung Kong
,
Gaoran Xu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17162803
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17162803
The spatial attention mechanism has been widely employed in the semantic segmentation of remote sensing images due to its exceptional capacity for modeling long-range dependencies. However, the analysis performance of remote sensing images can be reduced owing to their large intra-class variance and complex spatial structures. The vanilla spatial attention mechanism relies on the dense affine operations and a fixed scanning mechanism, which often introduces a large amount of redundant contextual semantic information and lacks consideration of cross-directional semantic connections. This paper proposes a new Cross-scan Semantic Cluster Network (CSCN) with integrated Semantic Filtering Contextual Cluster (SFCC) and Cross-scan Scene Coupling Attention (CSCA) modules to address these limitations. Specifically, the SFCC is designed to filter redundant information; feature tokens are clustered into semantically related regions, effectively identifying local features and reducing the impact of intra-class variance. CSCA effectively addresses the challenges of complex spatial geographic backgrounds by decomposing scene information into object distributions and global representations, using scene coupling and cross-scanning mechanisms and computing attention from different directions. Combining SFCC and CSCA, CSCN not only effectively segments various geographic spatial objects in complex scenes but also has low model complexity. The experimental results on three benchmark datasets demonstrate the outstanding performance of the attention model generated using this approach.
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- Type
- article
- Language
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- Landing Page
- https://doi.org/10.3390/rs17162803
- OA Status
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- References
- 70
- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4413164523Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs17162803Digital Object Identifier
- Title
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CSCN: A Cross-Scan Semantic Cluster Network with Scene Coupling Attention for Remote Sensing SegmentationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-13Full publication date if available
- Authors
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Lei Zhang, Xing Xing, Changfeng Jing, Min Jung Kong, Gaoran XuList of authors in order
- Landing page
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https://doi.org/10.3390/rs17162803Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/rs17162803Direct OA link when available
- Concepts
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Computer science, Segmentation, Cluster (spacecraft), Remote sensing, Artificial intelligence, Geography, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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70Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.are | 119 |
| abstract_inverted_index.but | 184 |
| abstract_inverted_index.can | 33 |
| abstract_inverted_index.due | 16 |
| abstract_inverted_index.for | 21 |
| abstract_inverted_index.has | 4, 186 |
| abstract_inverted_index.its | 18 |
| abstract_inverted_index.low | 187 |
| abstract_inverted_index.new | 84 |
| abstract_inverted_index.not | 173 |
| abstract_inverted_index.the | 9, 26, 53, 109, 131, 139, 198, 202 |
| abstract_inverted_index.CSCA | 136 |
| abstract_inverted_index.CSCN | 172 |
| abstract_inverted_index.SFCC | 110, 169 |
| abstract_inverted_index.This | 80 |
| abstract_inverted_index.also | 185 |
| abstract_inverted_index.been | 5 |
| abstract_inverted_index.from | 165 |
| abstract_inverted_index.into | 121, 150 |
| abstract_inverted_index.only | 174 |
| abstract_inverted_index.this | 207 |
| abstract_inverted_index.with | 90 |
| abstract_inverted_index.CSCA, | 171 |
| abstract_inverted_index.Scene | 99 |
| abstract_inverted_index.dense | 54 |
| abstract_inverted_index.fixed | 59 |
| abstract_inverted_index.lacks | 74 |
| abstract_inverted_index.large | 39, 66 |
| abstract_inverted_index.local | 127 |
| abstract_inverted_index.model | 188, 204 |
| abstract_inverted_index.often | 63 |
| abstract_inverted_index.owing | 36 |
| abstract_inverted_index.paper | 81 |
| abstract_inverted_index.scene | 148, 157 |
| abstract_inverted_index.their | 38 |
| abstract_inverted_index.these | 106 |
| abstract_inverted_index.three | 194 |
| abstract_inverted_index.using | 156, 206 |
| abstract_inverted_index.which | 62 |
| abstract_inverted_index.(CSCA) | 102 |
| abstract_inverted_index.(CSCN) | 89 |
| abstract_inverted_index.(SFCC) | 96 |
| abstract_inverted_index.affine | 55 |
| abstract_inverted_index.amount | 67 |
| abstract_inverted_index.filter | 114 |
| abstract_inverted_index.global | 154 |
| abstract_inverted_index.images | 15, 32 |
| abstract_inverted_index.impact | 132 |
| abstract_inverted_index.object | 151 |
| abstract_inverted_index.relies | 51 |
| abstract_inverted_index.remote | 13, 30 |
| abstract_inverted_index.scenes | 183 |
| abstract_inverted_index.tokens | 118 |
| abstract_inverted_index.widely | 6 |
| abstract_inverted_index.Cluster | 87, 95 |
| abstract_inverted_index.Network | 88 |
| abstract_inverted_index.address | 105 |
| abstract_inverted_index.complex | 43, 142, 182 |
| abstract_inverted_index.feature | 117 |
| abstract_inverted_index.modules | 103 |
| abstract_inverted_index.objects | 180 |
| abstract_inverted_index.reduced | 35 |
| abstract_inverted_index.related | 123 |
| abstract_inverted_index.results | 192 |
| abstract_inverted_index.sensing | 14, 31 |
| abstract_inverted_index.spatial | 1, 44, 48, 143, 179 |
| abstract_inverted_index.vanilla | 47 |
| abstract_inverted_index.various | 177 |
| abstract_inverted_index.Coupling | 100 |
| abstract_inverted_index.However, | 25 |
| abstract_inverted_index.Semantic | 86, 92 |
| abstract_inverted_index.analysis | 27 |
| abstract_inverted_index.capacity | 20 |
| abstract_inverted_index.coupling | 158 |
| abstract_inverted_index.datasets | 196 |
| abstract_inverted_index.designed | 112 |
| abstract_inverted_index.employed | 7 |
| abstract_inverted_index.features | 128 |
| abstract_inverted_index.modeling | 22 |
| abstract_inverted_index.proposes | 82 |
| abstract_inverted_index.reducing | 130 |
| abstract_inverted_index.regions, | 124 |
| abstract_inverted_index.scanning | 60 |
| abstract_inverted_index.segments | 176 |
| abstract_inverted_index.semantic | 10, 71, 78 |
| abstract_inverted_index.variance | 41 |
| abstract_inverted_index.Attention | 101 |
| abstract_inverted_index.Combining | 168 |
| abstract_inverted_index.Filtering | 93 |
| abstract_inverted_index.addresses | 138 |
| abstract_inverted_index.approach. | 208 |
| abstract_inverted_index.attention | 2, 49, 164, 203 |
| abstract_inverted_index.benchmark | 195 |
| abstract_inverted_index.clustered | 120 |
| abstract_inverted_index.computing | 163 |
| abstract_inverted_index.different | 166 |
| abstract_inverted_index.generated | 205 |
| abstract_inverted_index.mechanism | 3, 50 |
| abstract_inverted_index.redundant | 69, 115 |
| abstract_inverted_index.variance. | 135 |
| abstract_inverted_index.Contextual | 94 |
| abstract_inverted_index.Cross-scan | 85, 98 |
| abstract_inverted_index.challenges | 140 |
| abstract_inverted_index.contextual | 70 |
| abstract_inverted_index.geographic | 144, 178 |
| abstract_inverted_index.integrated | 91 |
| abstract_inverted_index.introduces | 64 |
| abstract_inverted_index.long-range | 23 |
| abstract_inverted_index.mechanism, | 61 |
| abstract_inverted_index.mechanisms | 161 |
| abstract_inverted_index.operations | 56 |
| abstract_inverted_index.backgrounds | 145 |
| abstract_inverted_index.complexity. | 189 |
| abstract_inverted_index.decomposing | 147 |
| abstract_inverted_index.demonstrate | 197 |
| abstract_inverted_index.directions. | 167 |
| abstract_inverted_index.effectively | 125, 137, 175 |
| abstract_inverted_index.exceptional | 19 |
| abstract_inverted_index.identifying | 126 |
| abstract_inverted_index.information | 72, 149 |
| abstract_inverted_index.intra-class | 40, 134 |
| abstract_inverted_index.outstanding | 199 |
| abstract_inverted_index.performance | 28, 200 |
| abstract_inverted_index.structures. | 45 |
| abstract_inverted_index.connections. | 79 |
| abstract_inverted_index.experimental | 191 |
| abstract_inverted_index.information; | 116 |
| abstract_inverted_index.limitations. | 107 |
| abstract_inverted_index.segmentation | 11 |
| abstract_inverted_index.semantically | 122 |
| abstract_inverted_index.Specifically, | 108 |
| abstract_inverted_index.consideration | 75 |
| abstract_inverted_index.dependencies. | 24 |
| abstract_inverted_index.distributions | 152 |
| abstract_inverted_index.cross-scanning | 160 |
| abstract_inverted_index.representations, | 155 |
| abstract_inverted_index.cross-directional | 77 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5009114126 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I3125743391 |
| citation_normalized_percentile.value | 0.45458353 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |