An Energy-Domain IR NUC Method Based on Unsupervised Learning
To obtain accurate blackbody temperature, emissivity, and waveband measurements, an energy-domain infrared nonuniformity method based on unsupervised learning is proposed. This method exploits the inherent physical correlation within the calibration dataset and sets the average predicted energy-doma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/187 |
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| author | Ting Li Xuefeng Lai Sheng Liao Yucheng Xia |
| author_facet | Ting Li Xuefeng Lai Sheng Liao Yucheng Xia |
| author_sort | Ting Li |
| collection | DOAJ |
| description | To obtain accurate blackbody temperature, emissivity, and waveband measurements, an energy-domain infrared nonuniformity method based on unsupervised learning is proposed. This method exploits the inherent physical correlation within the calibration dataset and sets the average predicted energy-domain value of the same blackbody temperature as the learning goal. Then, the coefficients of the model are learned without theoretical radiance labels by leveraging clustering-based unsupervised learning methodologies. Finally, several experiments are performed on a mid-wave infrared system. The results show that the trained correction network is uniform and produces stable outputs when the integration time and attenuator change within the optimal dynamic range. The maximum change in the image corrected using the proposed algorithm was 1.29%. |
| format | Article |
| id | doaj-art-3146955cf5ca49bfad7b282cc4a4c986 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-3146955cf5ca49bfad7b282cc4a4c9862025-01-24T13:47:40ZengMDPI AGRemote Sensing2072-42922025-01-0117218710.3390/rs17020187An Energy-Domain IR NUC Method Based on Unsupervised LearningTing Li0Xuefeng Lai1Sheng Liao2Yucheng Xia3National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Beijing 100049, ChinaNational Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, ChinaNational Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Beijing 100049, ChinaNational Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, ChinaTo obtain accurate blackbody temperature, emissivity, and waveband measurements, an energy-domain infrared nonuniformity method based on unsupervised learning is proposed. This method exploits the inherent physical correlation within the calibration dataset and sets the average predicted energy-domain value of the same blackbody temperature as the learning goal. Then, the coefficients of the model are learned without theoretical radiance labels by leveraging clustering-based unsupervised learning methodologies. Finally, several experiments are performed on a mid-wave infrared system. The results show that the trained correction network is uniform and produces stable outputs when the integration time and attenuator change within the optimal dynamic range. The maximum change in the image corrected using the proposed algorithm was 1.29%.https://www.mdpi.com/2072-4292/17/2/187infrared systemnonuniformity correctionunsupervised learningdynamic range adjustment |
| spellingShingle | Ting Li Xuefeng Lai Sheng Liao Yucheng Xia An Energy-Domain IR NUC Method Based on Unsupervised Learning Remote Sensing infrared system nonuniformity correction unsupervised learning dynamic range adjustment |
| title | An Energy-Domain IR NUC Method Based on Unsupervised Learning |
| title_full | An Energy-Domain IR NUC Method Based on Unsupervised Learning |
| title_fullStr | An Energy-Domain IR NUC Method Based on Unsupervised Learning |
| title_full_unstemmed | An Energy-Domain IR NUC Method Based on Unsupervised Learning |
| title_short | An Energy-Domain IR NUC Method Based on Unsupervised Learning |
| title_sort | energy domain ir nuc method based on unsupervised learning |
| topic | infrared system nonuniformity correction unsupervised learning dynamic range adjustment |
| url | https://www.mdpi.com/2072-4292/17/2/187 |
| work_keys_str_mv | AT tingli anenergydomainirnucmethodbasedonunsupervisedlearning AT xuefenglai anenergydomainirnucmethodbasedonunsupervisedlearning AT shengliao anenergydomainirnucmethodbasedonunsupervisedlearning AT yuchengxia anenergydomainirnucmethodbasedonunsupervisedlearning AT tingli energydomainirnucmethodbasedonunsupervisedlearning AT xuefenglai energydomainirnucmethodbasedonunsupervisedlearning AT shengliao energydomainirnucmethodbasedonunsupervisedlearning AT yuchengxia energydomainirnucmethodbasedonunsupervisedlearning |