Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion Article Swipe
Yice Cao
,
Chenchen Liu
,
Zhenhua Wu
,
Wenxin Yao
,
Liu Xiong
,
Jie Chen
,
Zhixiang Huang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.05624
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.05624
As remote sensing imaging technology continues to advance and evolve, processing high-resolution and diversified satellite imagery to improve segmentation accuracy and enhance interpretation efficiency emerg as a pivotal area of investigation within the realm of remote sensing. Although segmentation algorithms based on CNNs and Transformers achieve significant progress in performance, balancing segmentation accuracy and computational complexity remains challenging, limiting their wide application in practical tasks. To address this, this paper introduces state space model (SSM) and proposes a novel hybrid semantic segmentation network based on vision Mamba (CVMH-UNet). This method designs a cross-scanning visual state space block (CVSSBlock) that uses cross 2D scanning (CS2D) to fully capture global information from multiple directions, while by incorporating convolutional neural network branches to overcome the constraints of Vision Mamba (VMamba) in acquiring local information, this approach facilitates a comprehensive analysis of both global and local features. Furthermore, to address the issue of limited discriminative power and the difficulty in achieving detailed fusion with direct skip connections, a multi-frequency multi-scale feature fusion block (MFMSBlock) is designed. This module introduces multi-frequency information through 2D discrete cosine transform (2D DCT) to enhance information utilization and provides additional scale local detail information through point-wise convolution branches. Finally, it aggregates multi-scale information along the channel dimension, achieving refined feature fusion. Findings from experiments conducted on renowned datasets of remote sensing imagery demonstrate that proposed CVMH-UNet achieves superior segmentation performance while maintaining low computational complexity, outperforming surpassing current leading-edge segmentation algorithms.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.05624
- https://arxiv.org/pdf/2410.05624
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403346575
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403346575Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2410.05624Digital Object Identifier
- Title
-
Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature FusionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-08Full publication date if available
- Authors
-
Yice Cao, Chenchen Liu, Zhenhua Wu, Wenxin Yao, Liu Xiong, Jie Chen, Zhixiang HuangList of authors in order
- Landing page
-
https://arxiv.org/abs/2410.05624Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2410.05624Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2410.05624Direct OA link when available
- Concepts
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Artificial intelligence, Scale (ratio), Computer vision, Feature (linguistics), Computer science, Fusion, Segmentation, Image fusion, Image (mathematics), Remote sensing, Pattern recognition (psychology), Geography, Cartography, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.imagery | 15, 222 |
| abstract_inverted_index.imaging | 3 |
| abstract_inverted_index.improve | 17 |
| abstract_inverted_index.limited | 149 |
| abstract_inverted_index.network | 82, 117 |
| abstract_inverted_index.pivotal | 27 |
| abstract_inverted_index.refined | 209 |
| abstract_inverted_index.remains | 56 |
| abstract_inverted_index.sensing | 2, 221 |
| abstract_inverted_index.through | 177, 195 |
| abstract_inverted_index.(VMamba) | 126 |
| abstract_inverted_index.Although | 37 |
| abstract_inverted_index.Finally, | 199 |
| abstract_inverted_index.Findings | 212 |
| abstract_inverted_index.accuracy | 19, 52 |
| abstract_inverted_index.achieves | 227 |
| abstract_inverted_index.analysis | 136 |
| abstract_inverted_index.approach | 132 |
| abstract_inverted_index.branches | 118 |
| abstract_inverted_index.datasets | 218 |
| abstract_inverted_index.detailed | 157 |
| abstract_inverted_index.discrete | 179 |
| abstract_inverted_index.limiting | 58 |
| abstract_inverted_index.multiple | 110 |
| abstract_inverted_index.overcome | 120 |
| abstract_inverted_index.progress | 47 |
| abstract_inverted_index.proposed | 225 |
| abstract_inverted_index.proposes | 76 |
| abstract_inverted_index.provides | 189 |
| abstract_inverted_index.renowned | 217 |
| abstract_inverted_index.scanning | 102 |
| abstract_inverted_index.semantic | 80 |
| abstract_inverted_index.sensing. | 36 |
| abstract_inverted_index.superior | 228 |
| abstract_inverted_index.CVMH-UNet | 226 |
| abstract_inverted_index.achieving | 156, 208 |
| abstract_inverted_index.acquiring | 128 |
| abstract_inverted_index.balancing | 50 |
| abstract_inverted_index.branches. | 198 |
| abstract_inverted_index.conducted | 215 |
| abstract_inverted_index.continues | 5 |
| abstract_inverted_index.designed. | 171 |
| abstract_inverted_index.features. | 142 |
| abstract_inverted_index.practical | 63 |
| abstract_inverted_index.satellite | 14 |
| abstract_inverted_index.transform | 181 |
| abstract_inverted_index.additional | 190 |
| abstract_inverted_index.aggregates | 201 |
| abstract_inverted_index.algorithms | 39 |
| abstract_inverted_index.complexity | 55 |
| abstract_inverted_index.difficulty | 154 |
| abstract_inverted_index.dimension, | 207 |
| abstract_inverted_index.efficiency | 23 |
| abstract_inverted_index.introduces | 70, 174 |
| abstract_inverted_index.point-wise | 196 |
| abstract_inverted_index.processing | 10 |
| abstract_inverted_index.surpassing | 237 |
| abstract_inverted_index.technology | 4 |
| abstract_inverted_index.(CVSSBlock) | 97 |
| abstract_inverted_index.(MFMSBlock) | 169 |
| abstract_inverted_index.algorithms. | 241 |
| abstract_inverted_index.application | 61 |
| abstract_inverted_index.complexity, | 235 |
| abstract_inverted_index.constraints | 122 |
| abstract_inverted_index.convolution | 197 |
| abstract_inverted_index.demonstrate | 223 |
| abstract_inverted_index.directions, | 111 |
| abstract_inverted_index.diversified | 13 |
| abstract_inverted_index.experiments | 214 |
| abstract_inverted_index.facilitates | 133 |
| abstract_inverted_index.information | 108, 176, 186, 194, 203 |
| abstract_inverted_index.maintaining | 232 |
| abstract_inverted_index.multi-scale | 165, 202 |
| abstract_inverted_index.performance | 230 |
| abstract_inverted_index.significant | 46 |
| abstract_inverted_index.utilization | 187 |
| abstract_inverted_index.(CVMH-UNet). | 87 |
| abstract_inverted_index.Furthermore, | 143 |
| abstract_inverted_index.Transformers | 44 |
| abstract_inverted_index.challenging, | 57 |
| abstract_inverted_index.connections, | 162 |
| abstract_inverted_index.information, | 130 |
| abstract_inverted_index.leading-edge | 239 |
| abstract_inverted_index.performance, | 49 |
| abstract_inverted_index.segmentation | 18, 38, 51, 81, 229, 240 |
| abstract_inverted_index.comprehensive | 135 |
| abstract_inverted_index.computational | 54, 234 |
| abstract_inverted_index.convolutional | 115 |
| abstract_inverted_index.incorporating | 114 |
| abstract_inverted_index.investigation | 30 |
| abstract_inverted_index.outperforming | 236 |
| abstract_inverted_index.cross-scanning | 92 |
| abstract_inverted_index.discriminative | 150 |
| abstract_inverted_index.interpretation | 22 |
| abstract_inverted_index.high-resolution | 11 |
| abstract_inverted_index.multi-frequency | 164, 175 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |