Adaptive-Labeling for Enhancing Remote Sensing Cloud Understanding Article Swipe
Jay Gala
,
Sauradip Nag
,
Huichou Huang
,
Ruirui Liu
,
Xiatian Zhu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.05198
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.05198
Cloud analysis is a critical component of weather and climate science, impacting various sectors like disaster management. However, achieving fine-grained cloud analysis, such as cloud segmentation, in remote sensing remains challenging due to the inherent difficulties in obtaining accurate labels, leading to significant labeling errors in training data. Existing methods often assume the availability of reliable segmentation annotations, limiting their overall performance. To address this inherent limitation, we introduce an innovative model-agnostic Cloud Adaptive-Labeling (CAL) approach, which operates iteratively to enhance the quality of training data annotations and consequently improve the performance of the learned model. Our methodology commences by training a cloud segmentation model using the original annotations. Subsequently, it introduces a trainable pixel intensity threshold for adaptively labeling the cloud training images on the fly. The newly generated labels are then employed to fine-tune the model. Extensive experiments conducted on multiple standard cloud segmentation benchmarks demonstrate the effectiveness of our approach in significantly boosting the performance of existing segmentation models. Our CAL method establishes new state-of-the-art results when compared to a wide array of existing alternatives.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2311.05198
- https://arxiv.org/pdf/2311.05198
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388585878
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388585878Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2311.05198Digital Object Identifier
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Adaptive-Labeling for Enhancing Remote Sensing Cloud UnderstandingWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-11-09Full publication date if available
- Authors
-
Jay Gala, Sauradip Nag, Huichou Huang, Ruirui Liu, Xiatian ZhuList of authors in order
- Landing page
-
https://arxiv.org/abs/2311.05198Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2311.05198Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2311.05198Direct OA link when available
- Concepts
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Cloud computing, Segmentation, Computer science, Boosting (machine learning), Image segmentation, Limiting, Data mining, Artificial intelligence, Machine learning, Engineering, Mechanical engineering, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile |