Subpixel Temperature Estimation by Information Transfer With Adaptive Ensemble Extreme Learning Machine (IT-AEELM) Article Swipe
Yue Hu
,
Xinyu Zhou
,
Ye Zhang
,
Shaoqi Shi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2021.3091125
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2021.3091125
The retrieval of land surface temperature (LST) using thermal infrared (TIR) data is important in many applications. However, TIR data usually suffer from low spatial resolution. We introduce a novel subpixel LST estimation model using the information-transfer-based adaptive ensemble extreme learning machine (IT-AEELM). The proposed method constructs a reliable relationship between subpixel LST and the input high-resolution visible and near-infrared (VNIR) data, short-wave infrared (SWIR) data, and low-resolution TIR data. Based on a detailed analysis of different ground objects, we divide the input data into multiple subsets. Instead of using consistent land surface parameters (LSPs), we utilize different LSPs to characterize the land surface properties in each subset. The VNIR-SWIR-LSPs data and the low-resolution LST are used to train a novel IT-AEELM network, where a feedback ensemble learning scheme is introduced to effectively remove inaccurate estimates. The main difference of the model against existing methods is that it builds a robust architecture at different spatial scales, which provides benefits including lower demand for training data, more rapid and accurate acquisition of subpixel LST, and better adaption to heterogeneous land surface. Numerical experiments demonstrate that the proposed method significantly improves the accuracy of subpixel LST compared with the state-of-the-art algorithms.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2021.3091125
- https://ieeexplore.ieee.org/ielx7/4609443/9314330/09462392.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3174305608
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3174305608Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/jstars.2021.3091125Digital Object Identifier
- Title
-
Subpixel Temperature Estimation by Information Transfer With Adaptive Ensemble Extreme Learning Machine (IT-AEELM)Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Yue Hu, Xinyu Zhou, Ye Zhang, Shaoqi ShiList of authors in order
- Landing page
-
https://doi.org/10.1109/jstars.2021.3091125Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/4609443/9314330/09462392.pdfDirect link to full text PDF
- 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://ieeexplore.ieee.org/ielx7/4609443/9314330/09462392.pdfDirect OA link when available
- Concepts
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Subpixel rendering, VNIR, Computer science, Image resolution, Remote sensing, Artificial intelligence, Transfer of learning, Data modeling, Pixel, Machine learning, Pattern recognition (psychology), Data mining, Hyperspectral imaging, Database, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
- References (count)
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44Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
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| publication_date | 2021-01-01 |
| publication_year | 2021 |
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