Multimodal Contrastive Learning for Remote Sensing Image Feature Extraction Based on Relaxed Positive Samples Article Swipe
Zhenshi Zhang
,
Qiujun Li
,
Wenxuan Jing
,
Guangjun He
,
Lili Zhu
,
Shijuan Gao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24237719
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24237719
Traditional multimodal contrastive learning brings text and its corresponding image closer together as a positive pair, where the text typically consists of fixed sentence structures or specific descriptive statements, and the image features are generally global features (with some fine-grained work using local features). Similar to unimodal self-supervised contrastive learning, this approach can be seen as enforcing a strict identity constraint in a multimodal context. However, due to the inherent complexity of remote sensing images, which cannot be easily described in a single sentence, and the fact that remote sensing images contain rich ancillary information beyond just object features, this strict identity constraint may be insufficient. To fully leverage the characteristics of remote sensing images, we propose a multimodal contrastive learning method for remote sensing image feature extraction, based on positive sample tripartite relaxation, where the model is relaxed in three aspects. The first aspect of relaxation involves both the text and image inputs. By introducing learnable parameters in the language and image branches, instead of relying on fixed sentence structures and fixed image features, the network can achieve a more flexible description of remote sensing images in text and extract ancillary information from the image features, thereby relaxing the input constraints. Second relaxation is achieved through multimodal alignment of various features. By aligning semantic information with the corresponding semantic regions in the images, the method allows for the relaxation of local image features under semantic constraints. This approach addresses the issue of selecting image patches in unimodal settings, where there is no semantic constraint. The proposed method for remote sensing image feature extraction has been validated on four datasets. On the PatternNet dataset, it achieved a 91.1% accuracy with just one-shot.
Related Topics
Concepts
Computer science
Artificial intelligence
Leverage (statistics)
Sentence
Constraint (computer-aided design)
Feature (linguistics)
Feature extraction
Pattern recognition (psychology)
Feature learning
Context (archaeology)
Image (mathematics)
Natural language processing
Computer vision
Mathematics
Linguistics
Geography
Geometry
Archaeology
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s24237719
- OA Status
- gold
- Cited By
- 4
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405001323
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405001323Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s24237719Digital Object Identifier
- Title
-
Multimodal Contrastive Learning for Remote Sensing Image Feature Extraction Based on Relaxed Positive SamplesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-03Full publication date if available
- Authors
-
Zhenshi Zhang, Qiujun Li, Wenxuan Jing, Guangjun He, Lili Zhu, Shijuan GaoList of authors in order
- Landing page
-
https://doi.org/10.3390/s24237719Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/s24237719Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Leverage (statistics), Sentence, Constraint (computer-aided design), Feature (linguistics), Feature extraction, Pattern recognition (psychology), Feature learning, Context (archaeology), Image (mathematics), Natural language processing, Computer vision, Mathematics, Linguistics, Geography, Geometry, Archaeology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
- References (count)
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43Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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