MASANet: Multi-Angle Self-Attention Network for Semantic Segmentation of Remote Sensing Images Article Swipe
YOU?
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· 2022
· Open Access
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· DOI: https://doi.org/10.17559/tv-20220421142959
As an important research direction in the field of pattern recognition, semantic segmentation has become an important method for remote sensing image information extraction. However, due to the loss of global context information, the effect of semantic segmentation is still incomplete or misclassified. In this paper, we propose a multi-angle self-attention network (MASANet) to solve this problem. Specifically, we design a multi-angle self-attention module to enhance global context information, which uses three angles to enhance features and takes the obtained three features as the inputs of self-attention to further extract the global dependencies of features. In addition, atrous spatial pyramid pooling (ASPP) and global average pooling (GAP) further improve the overall performance. Finally, we concatenate the feature maps of different scales obtained in the feature extraction stage with the corresponding feature maps output by ASPP to further extract multi-scale features. The experimental results show that MASANet achieves good segmentation performance on high-resolution remote sensing images. In addition, the comparative experimental results show that MASANet is superior to some state-of-the-art models in terms of some widely used evaluation criteria.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17559/tv-20220421142959
- https://hrcak.srce.hr/file/408380
- OA Status
- gold
- Cited By
- 6
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4294539227
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4294539227Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.17559/tv-20220421142959Digital Object Identifier
- Title
-
MASANet: Multi-Angle Self-Attention Network for Semantic Segmentation of Remote Sensing ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-03Full publication date if available
- Authors
-
Fuping Zeng, Bin Yang, Mengci Zhao, Ying Xing, Yiran MaList of authors in order
- Landing page
-
https://doi.org/10.17559/tv-20220421142959Publisher landing page
- PDF URL
-
https://hrcak.srce.hr/file/408380Direct link to full text PDF
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-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://hrcak.srce.hr/file/408380Direct OA link when available
- Concepts
-
Segmentation, Computer science, Remote sensing, Artificial intelligence, Computer vision, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 4Per-year citation counts (last 5 years)
- References (count)
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26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.In | 43, 95, 155 |
| abstract_inverted_index.an | 1, 15 |
| abstract_inverted_index.as | 82 |
| abstract_inverted_index.by | 133 |
| abstract_inverted_index.in | 5, 122, 170 |
| abstract_inverted_index.is | 38, 164 |
| abstract_inverted_index.of | 8, 29, 35, 85, 93, 118, 172 |
| abstract_inverted_index.on | 150 |
| abstract_inverted_index.or | 41 |
| abstract_inverted_index.to | 26, 53, 64, 73, 87, 135, 166 |
| abstract_inverted_index.we | 46, 58, 113 |
| abstract_inverted_index.The | 140 |
| abstract_inverted_index.and | 76, 102 |
| abstract_inverted_index.due | 25 |
| abstract_inverted_index.for | 18 |
| abstract_inverted_index.has | 13 |
| abstract_inverted_index.the | 6, 27, 33, 78, 83, 90, 109, 115, 123, 128, 157 |
| abstract_inverted_index.ASPP | 134 |
| abstract_inverted_index.good | 147 |
| abstract_inverted_index.loss | 28 |
| abstract_inverted_index.maps | 117, 131 |
| abstract_inverted_index.show | 143, 161 |
| abstract_inverted_index.some | 167, 173 |
| abstract_inverted_index.that | 144, 162 |
| abstract_inverted_index.this | 44, 55 |
| abstract_inverted_index.used | 175 |
| abstract_inverted_index.uses | 70 |
| abstract_inverted_index.with | 127 |
| abstract_inverted_index.(GAP) | 106 |
| abstract_inverted_index.field | 7 |
| abstract_inverted_index.image | 21 |
| abstract_inverted_index.solve | 54 |
| abstract_inverted_index.stage | 126 |
| abstract_inverted_index.still | 39 |
| abstract_inverted_index.takes | 77 |
| abstract_inverted_index.terms | 171 |
| abstract_inverted_index.three | 71, 80 |
| abstract_inverted_index.which | 69 |
| abstract_inverted_index.(ASPP) | 101 |
| abstract_inverted_index.angles | 72 |
| abstract_inverted_index.atrous | 97 |
| abstract_inverted_index.become | 14 |
| abstract_inverted_index.design | 59 |
| abstract_inverted_index.effect | 34 |
| abstract_inverted_index.global | 30, 66, 91, 103 |
| abstract_inverted_index.inputs | 84 |
| abstract_inverted_index.method | 17 |
| abstract_inverted_index.models | 169 |
| abstract_inverted_index.module | 63 |
| abstract_inverted_index.output | 132 |
| abstract_inverted_index.paper, | 45 |
| abstract_inverted_index.remote | 19, 152 |
| abstract_inverted_index.scales | 120 |
| abstract_inverted_index.widely | 174 |
| abstract_inverted_index.MASANet | 145, 163 |
| abstract_inverted_index.average | 104 |
| abstract_inverted_index.context | 31, 67 |
| abstract_inverted_index.enhance | 65, 74 |
| abstract_inverted_index.extract | 89, 137 |
| abstract_inverted_index.feature | 116, 124, 130 |
| abstract_inverted_index.further | 88, 107, 136 |
| abstract_inverted_index.images. | 154 |
| abstract_inverted_index.improve | 108 |
| abstract_inverted_index.network | 51 |
| abstract_inverted_index.overall | 110 |
| abstract_inverted_index.pattern | 9 |
| abstract_inverted_index.pooling | 100, 105 |
| abstract_inverted_index.propose | 47 |
| abstract_inverted_index.pyramid | 99 |
| abstract_inverted_index.results | 142, 160 |
| abstract_inverted_index.sensing | 20, 153 |
| abstract_inverted_index.spatial | 98 |
| abstract_inverted_index.Finally, | 112 |
| abstract_inverted_index.However, | 24 |
| abstract_inverted_index.achieves | 146 |
| abstract_inverted_index.features | 75, 81 |
| abstract_inverted_index.obtained | 79, 121 |
| abstract_inverted_index.problem. | 56 |
| abstract_inverted_index.research | 3 |
| abstract_inverted_index.semantic | 11, 36 |
| abstract_inverted_index.superior | 165 |
| abstract_inverted_index.(MASANet) | 52 |
| abstract_inverted_index.addition, | 96, 156 |
| abstract_inverted_index.criteria. | 177 |
| abstract_inverted_index.different | 119 |
| abstract_inverted_index.direction | 4 |
| abstract_inverted_index.features. | 94, 139 |
| abstract_inverted_index.important | 2, 16 |
| abstract_inverted_index.evaluation | 176 |
| abstract_inverted_index.extraction | 125 |
| abstract_inverted_index.incomplete | 40 |
| abstract_inverted_index.comparative | 158 |
| abstract_inverted_index.concatenate | 114 |
| abstract_inverted_index.extraction. | 23 |
| abstract_inverted_index.information | 22 |
| abstract_inverted_index.multi-angle | 49, 61 |
| abstract_inverted_index.multi-scale | 138 |
| abstract_inverted_index.performance | 149 |
| abstract_inverted_index.dependencies | 92 |
| abstract_inverted_index.experimental | 141, 159 |
| abstract_inverted_index.information, | 32, 68 |
| abstract_inverted_index.performance. | 111 |
| abstract_inverted_index.recognition, | 10 |
| abstract_inverted_index.segmentation | 12, 37, 148 |
| abstract_inverted_index.Specifically, | 57 |
| abstract_inverted_index.corresponding | 129 |
| abstract_inverted_index.misclassified. | 42 |
| abstract_inverted_index.self-attention | 50, 62, 86 |
| abstract_inverted_index.high-resolution | 151 |
| abstract_inverted_index.state-of-the-art | 168 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.7316746 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |