Microwave Radiometer Calibration Using Deep Learning With Reduced Reference Information and 2-D Spectral Features
The accuracy of geophysical retrievals from radiometers relies on calibration quality, encompassing both absolute radiometric accuracy and spectral consistency. Radiometers have employed various calibration techniques, including external targets, vicarious sources, and internal calibrators like nois...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10318952/ |
| _version_ | 1827593011142328320 |
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| author | Ahmed Manavi Alam Mehmet Kurum Mehmet Ogut Ali C. Gurbuz |
| author_facet | Ahmed Manavi Alam Mehmet Kurum Mehmet Ogut Ali C. Gurbuz |
| author_sort | Ahmed Manavi Alam |
| collection | DOAJ |
| description | The accuracy of geophysical retrievals from radiometers relies on calibration quality, encompassing both absolute radiometric accuracy and spectral consistency. Radiometers have employed various calibration techniques, including external targets, vicarious sources, and internal calibrators like noise diodes or matched reference loads. Calibration techniques face challenges like frequency dependence, instrumental effects, environmental influences, drift, aging, and radio frequency interference. Recent hardware advancements enable radiometers to collect raw samples containing both temporal and spectral information. Leveraging advanced modeling techniques like deep learning (DL) enables detecting subtle correlations, non-linear dependencies, and higher-order interactions within the data extracting valuable information that may have been challenging with conventional methods. This study utilizes NASA's Soil Moisture Active Passive (SMAP) satellite's level 1A and level 1B data products to develop a DL-based radiometer calibrator to estimate antenna temperature. Spectrograms of second raw moments equivalent to power carrying the 2-D spectral features serve as primary input in a supervised convolutional neural network-based architecture. DL-based calibrator has demonstrated high correlation and low root mean square error when incorporating spectral information from both reference and noise diodes and when not considering this information. Findings suggest that the ancillary features such as internal thermistor temperature and loss elements exhibit sufficient accuracy in estimating antenna temperature to compensate for variations in receiver noise temperature and short-term gain fluctuations in the absence of the reference load and noise diode power. The proposed calibration technique with reduced reference information might enable radiometers for a higher number of antenna scene observations within a footprint. |
| first_indexed | 2024-03-09T02:03:59Z |
| format | Article |
| id | doaj.art-a1571414c43e4d6ba320b7ff8cba9a4c |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-09T02:03:59Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-a1571414c43e4d6ba320b7ff8cba9a4c2023-12-08T00:02:03ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352024-01-011774876510.1109/JSTARS.2023.333326810318952Microwave Radiometer Calibration Using Deep Learning With Reduced Reference Information and 2-D Spectral FeaturesAhmed Manavi Alam0https://orcid.org/0000-0003-2022-9761Mehmet Kurum1https://orcid.org/0000-0002-5750-9014Mehmet Ogut2https://orcid.org/0000-0002-7142-6899Ali C. Gurbuz3https://orcid.org/0000-0001-8923-0299Department of Electrical and Computer Engineering, and Information Processing and Sensing Lab, Mississippi State University, Mississippi State, MS, USASchool of Electrical and Computer Engineering, University of Georgia, Athens, GA, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USADepartment of Electrical and Computer Engineering, and Information Processing and Sensing Lab, Mississippi State University, Mississippi State, MS, USAThe accuracy of geophysical retrievals from radiometers relies on calibration quality, encompassing both absolute radiometric accuracy and spectral consistency. Radiometers have employed various calibration techniques, including external targets, vicarious sources, and internal calibrators like noise diodes or matched reference loads. Calibration techniques face challenges like frequency dependence, instrumental effects, environmental influences, drift, aging, and radio frequency interference. Recent hardware advancements enable radiometers to collect raw samples containing both temporal and spectral information. Leveraging advanced modeling techniques like deep learning (DL) enables detecting subtle correlations, non-linear dependencies, and higher-order interactions within the data extracting valuable information that may have been challenging with conventional methods. This study utilizes NASA's Soil Moisture Active Passive (SMAP) satellite's level 1A and level 1B data products to develop a DL-based radiometer calibrator to estimate antenna temperature. Spectrograms of second raw moments equivalent to power carrying the 2-D spectral features serve as primary input in a supervised convolutional neural network-based architecture. DL-based calibrator has demonstrated high correlation and low root mean square error when incorporating spectral information from both reference and noise diodes and when not considering this information. Findings suggest that the ancillary features such as internal thermistor temperature and loss elements exhibit sufficient accuracy in estimating antenna temperature to compensate for variations in receiver noise temperature and short-term gain fluctuations in the absence of the reference load and noise diode power. The proposed calibration technique with reduced reference information might enable radiometers for a higher number of antenna scene observations within a footprint.https://ieeexplore.ieee.org/document/10318952/Calibrationdeep learning (DL)machine learningmicrowave radiometryneural networkradio frequency interference (RFI) |
| spellingShingle | Ahmed Manavi Alam Mehmet Kurum Mehmet Ogut Ali C. Gurbuz Microwave Radiometer Calibration Using Deep Learning With Reduced Reference Information and 2-D Spectral Features IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Calibration deep learning (DL) machine learning microwave radiometry neural network radio frequency interference (RFI) |
| title | Microwave Radiometer Calibration Using Deep Learning With Reduced Reference Information and 2-D Spectral Features |
| title_full | Microwave Radiometer Calibration Using Deep Learning With Reduced Reference Information and 2-D Spectral Features |
| title_fullStr | Microwave Radiometer Calibration Using Deep Learning With Reduced Reference Information and 2-D Spectral Features |
| title_full_unstemmed | Microwave Radiometer Calibration Using Deep Learning With Reduced Reference Information and 2-D Spectral Features |
| title_short | Microwave Radiometer Calibration Using Deep Learning With Reduced Reference Information and 2-D Spectral Features |
| title_sort | microwave radiometer calibration using deep learning with reduced reference information and 2 d spectral features |
| topic | Calibration deep learning (DL) machine learning microwave radiometry neural network radio frequency interference (RFI) |
| url | https://ieeexplore.ieee.org/document/10318952/ |
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