Semantic Relation Model and Dataset for Remote Sensing Scene Understanding Article Swipe
Peng Li
,
Dezheng Zhang
,
Aziguli Wulamu
,
Xin Liu
,
Peng Chen
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi10070488
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi10070488
A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijgi10070488
- https://www.mdpi.com/2220-9964/10/7/488/pdf?version=1626489989
- OA Status
- gold
- Cited By
- 14
- References
- 87
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3186819486
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3186819486Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/ijgi10070488Digital Object Identifier
- Title
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Semantic Relation Model and Dataset for Remote Sensing Scene UnderstandingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-07-17Full publication date if available
- Authors
-
Peng Li, Dezheng Zhang, Aziguli Wulamu, Xin Liu, Peng ChenList of authors in order
- Landing page
-
https://doi.org/10.3390/ijgi10070488Publisher landing page
- PDF URL
-
https://www.mdpi.com/2220-9964/10/7/488/pdf?version=1626489989Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2220-9964/10/7/488/pdf?version=1626489989Direct OA link when available
- Concepts
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Computer science, Graph, Artificial intelligence, Semantic gap, Context (archaeology), Image (mathematics), Theoretical computer science, Image retrieval, Paleontology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 4, 2023: 2, 2022: 3Per-year citation counts (last 5 years)
- References (count)
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87Number 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.A | 0 |
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| abstract_inverted_index.In | 69, 81, 193 |
| abstract_inverted_index.We | 173 |
| abstract_inverted_index.an | 10, 96 |
| abstract_inverted_index.in | 158 |
| abstract_inverted_index.is | 7, 37, 59, 86, 107 |
| abstract_inverted_index.of | 3, 16, 46, 56, 65, 114, 145, 155, 208 |
| abstract_inverted_index.on | 13, 95, 119, 189 |
| abstract_inverted_index.so | 38 |
| abstract_inverted_index.to | 61, 99, 109, 135, 150 |
| abstract_inverted_index.we | 72, 128 |
| abstract_inverted_index.and | 18, 49, 101, 125, 141, 178, 205 |
| abstract_inverted_index.are | 44 |
| abstract_inverted_index.for | 29 |
| abstract_inverted_index.gap | 201 |
| abstract_inverted_index.our | 4, 115, 183 |
| abstract_inverted_index.out | 175 |
| abstract_inverted_index.the | 19, 35, 41, 54, 63, 111, 120, 143, 153, 179, 198 |
| abstract_inverted_index.also | 23, 164 |
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| abstract_inverted_index.this | 70, 82, 162 |
| abstract_inverted_index.(GCN) | 93 |
| abstract_inverted_index.RSSGD | 195 |
| abstract_inverted_index.among | 139 |
| abstract_inverted_index.based | 94, 118 |
| abstract_inverted_index.carry | 174 |
| abstract_inverted_index.graph | 90, 147, 170, 191 |
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| abstract_inverted_index.paper | 163 |
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| abstract_inverted_index.field, | 161 |
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| abstract_inverted_index.module | 134 |
| abstract_inverted_index.paper, | 71 |
| abstract_inverted_index.refine | 102 |
| abstract_inverted_index.remote | 32, 66, 159, 167, 209 |
| abstract_inverted_index.remove | 136 |
| abstract_inverted_index.series | 15 |
| abstract_inverted_index.sparse | 131 |
| abstract_inverted_index.visual | 5, 123 |
| abstract_inverted_index.(MSFN). | 80 |
| abstract_inverted_index.ability | 113 |
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| abstract_inverted_index.spatial | 51 |
| abstract_inverted_index.various | 42 |
| abstract_inverted_index.(RSSGD). | 172 |
| abstract_inverted_index.Besides, | 117 |
| abstract_inverted_index.context, | 105 |
| abstract_inverted_index.entities | 140 |
| abstract_inverted_index.features | 124 |
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