DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classification Article Swipe
Daniel F. S. Santos
,
Rafael G. Pires
,
Leandro A. Passos
,
João Paulo Papa
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/igarss47720.2021.9554277
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/igarss47720.2021.9554277
Research on remote sensing image classification significantly impacts\nessential human routine tasks such as urban planning and agriculture. Nowadays,\nthe rapid advance in technology and the availability of many high-quality\nremote sensing images create a demand for reliable automation methods. The\ncurrent paper proposes two novel deep learning-based architectures for image\nclassification purposes, i.e., the Discriminant Deep Image Prior Network and\nthe Discriminant Deep Image Prior Network+, which combine Deep Image Prior and\nTriplet Networks learning strategies. Experiments conducted over three\nwell-known public remote sensing image datasets achieved state-of-the-art\nresults, evidencing the effectiveness of using deep image priors for remote\nsensing image classification.\n
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/igarss47720.2021.9554277
- OA Status
- green
- Cited By
- 4
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3207667039
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3207667039Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/igarss47720.2021.9554277Digital Object Identifier
- Title
-
DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image ClassificationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-07-11Full publication date if available
- Authors
-
Daniel F. S. Santos, Rafael G. Pires, Leandro A. Passos, João Paulo PapaList of authors in order
- Landing page
-
https://doi.org/10.1109/igarss47720.2021.9554277Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2212.10411Direct OA link when available
- Concepts
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Artificial intelligence, Computer science, Contextual image classification, Deep learning, Image (mathematics), Linear discriminant analysis, Pattern recognition (psychology), Automation, Discriminant, Computer vision, Image quality, Remote sensing, Geography, Engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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23Number 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.Deep | 51, 57, 63 |
| abstract_inverted_index.deep | 42, 86 |
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| abstract_inverted_index.over | 72 |
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| abstract_inverted_index.Prior | 53, 59, 65 |
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| abstract_inverted_index.tasks | 10 |
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| abstract_inverted_index.sensing | 3, 28, 76 |
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| abstract_inverted_index.Research | 0 |
| abstract_inverted_index.achieved | 79 |
| abstract_inverted_index.and\nthe | 55 |
| abstract_inverted_index.datasets | 78 |
| abstract_inverted_index.learning | 68 |
| abstract_inverted_index.methods. | 36 |
| abstract_inverted_index.planning | 14 |
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| abstract_inverted_index.Network+, | 60 |
| abstract_inverted_index.conducted | 71 |
| abstract_inverted_index.purposes, | 47 |
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| abstract_inverted_index.Experiments | 70 |
| abstract_inverted_index.strategies. | 69 |
| abstract_inverted_index.Discriminant | 50, 56 |
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| abstract_inverted_index.agriculture. | 16 |
| abstract_inverted_index.and\nTriplet | 66 |
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| abstract_inverted_index.image\nclassification | 46 |
| abstract_inverted_index.state-of-the-art\nresults, | 80 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.65568175 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |