An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution Article Swipe
Naveed Sultan
,
Amir Hajian
,
Supavadee Aramvith
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.04595
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.04595
In recent years, convolutional neural networks (CNNs) have achieved remarkable advancement in the field of remote sensing image super-resolution due to the complexity and variability of textures and structures in remote sensing images (RSIs), which often repeat in the same images but differ across others. Current deep learning-based super-resolution models focus less on high-frequency features, which leads to suboptimal performance in capturing contours, textures, and spatial information. State-of-the-art CNN-based methods now focus on the feature extraction of RSIs using attention mechanisms. However, these methods are still incapable of effectively identifying and utilizing key content attention signals in RSIs. To solve this problem, we proposed an advanced feature extraction module called Channel and Spatial Attention Feature Extraction (CSA-FE) for effectively extracting the features by using the channel and spatial attention incorporated with the standard vision transformer (ViT). The proposed method trained over the UCMerced dataset on scales 2, 3, and 4. The experimental results show that our proposed method helps the model focus on the specific channels and spatial locations containing high-frequency information so that the model can focus on relevant features and suppress irrelevant ones, which enhances the quality of super-resolved images. Our model achieved superior performance compared to various existing models.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2405.04595
- https://arxiv.org/pdf/2405.04595
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396815837
All OpenAlex metadata
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https://openalex.org/W4396815837Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2405.04595Digital Object Identifier
- Title
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An Advanced Features Extraction Module for Remote Sensing Image Super-ResolutionWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-07Full publication date if available
- Authors
-
Naveed Sultan, Amir Hajian, Supavadee AramvithList of authors in order
- Landing page
-
https://arxiv.org/abs/2405.04595Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2405.04595Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2405.04595Direct OA link when available
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Extraction (chemistry), Computer science, Computer vision, Image (mathematics), Remote sensing, Artificial intelligence, Resolution (logic), Geography, Chromatography, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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