Review on Infrared Imaging Technology
Abstract
:1. Introduction
2. Infrared Thermal Imagers
Components of an Infrared Thermal Imager
3. Thermal Imaging Camera Processing Technology
3.1. Infrared Image Processing Technology
3.1.1. Non-Uniformity Correction for Infrared Images
- Nonuniformity correction of infrared image based on two-point calibration [28]
- 2.
- Nonuniformity correction of infrared image based on multi-point calibration [28]
- 3.
- Nonuniformity correction of infrared image based on BP neural network
3.1.2. Infrared Image Denoising
- Infrared image denoising based on depth learning [41]
3.1.3. Infrared Image Enhancement
3.2. Detection and Compensation of Blind Elements of Thermal Imaging Camera
3.2.1. Blind Element Detection
3.2.2. Blind Element Compensation
3.3. Infrared Thermal Imaging Temperature Measurement Technology
3.3.1. Principle of Temperature Measurement
3.3.2. Calibration of Infrared Thermal Imager [13]
- Theoretically, a black body is able to completely absorb electromagnetic waves of various wavelengths of radiation, there is no reflection and transmission, and its absorption ratio is 1;
- Structurally, radiation in an isothermal cavity with a small hole is black-body radiation. When the radiated electromagnetic wave is incident from the pore, various reflections occur in the cavity, and each reflected radiation will be absorbed part of the energy, and finally only a very small amount of energy escapes from the pore.
- From the application point of view, if a small hole is opened in the closed isothermal airborne, it can realistically simulate black-body radiation, which is called a black-body furnace. At a certain temperature, the black body is the object with the greatest ability to radiate, so it is also called a complete radiator [16].
3.4. Infrared Thermal Imaging Target Detection and Tracking
4. Multi/Hyperspectral Thermal Infrared Remote Sensing
Multi/Hyperspectral Thermal Infrared Remote Sensing Technology
5. Application of Sensor Processing Technology
5.1. Application of Infrared Thermal Imaging Processing Technology
5.2. Application of Multi/Hyperspectral Remote Sensing Technology
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Ma, T. Analysis of the principle and application of infrared thermal imager. In Proceedings of the 14th Ningxia Young Scientists Forum on Petrochemical Topics, Ningxia, China, 24 July 2018; pp. 323–325, 329. [Google Scholar]
- Li, S.Q.; Gong, Y.; Yang, Z.H.; Chen, J.P. Properties and thermal effects of sodium under infrared thermal imager. Chem. Teach. 2022, 2, 74–77. [Google Scholar]
- Huang, J.Q.; Wang, H.; Han, J.; Liu, X.H. Research on the application of hyperspectral remote sensing technology in the geological field–Comment on Modeling and application of hyperspectral remote sensing geological process. Nonferr. Met. Eng. 2022, 12, 145. [Google Scholar]
- Wang, Y.L. Analysis of agricultural low altitude hyperspectral remote sensing technology based on rotorcraft. Nanfang Agric. Mach. 2022, 53, 81–83. [Google Scholar]
- Zhao, G.P.; Wu, J.; Chen, J.Y.; Xu, F.R.; Li, X.W. Analysis of the application of hyperspectral remote sensing technology in the research of medicinal plants. Chin. J. Exp. Formul. 2022, 15, 1–10. [Google Scholar] [CrossRef]
- Xu, D.G.; Xing, X.W.; Li, Y.T.; Wang, C.; Tang, D.; Ye, H.Y. River oil spill monitoring based on UAV hyperspectral remote sensing technology. Pet. Nat. Gas Chem. Ind. 2019, 48, 93–97, 104. [Google Scholar]
- Li, Y.H.; Li, H.K.; Xu, F. Research progress of hyperspectral remote sensing monitoring of mine environmental pollution. Nonferr. Met. Sci. Eng. 2022, 13, 108–114. [Google Scholar] [CrossRef]
- Chen, Y.Y. Study on the application effect of breast cancer examination based on infrared thermal imager. Infrared 2021, 42, 43–49. [Google Scholar]
- Bian, Z.Y. Research on the application of infrared imaging method to detect the defects of exterior walls of residential buildings. Sichuan Cem. 2021, 7, 99–100, 136. [Google Scholar]
- Li, X.J.; Tu, W.W.; Sun, G.C.; Li, W.S.; Li, X.G. Analysis of components of grid connected photovoltaic power station detected by infrared thermal imager. China Insp. Test. 2022, 30, 17–20. [Google Scholar] [CrossRef]
- Sun, G.X.; Liu, J.; Zhang, L.T. Application prospect of thermal imager in aviation line maintenance. In Proceedings of the Symposium on Aviation Equipment Maintenance Technology and Application, Shandong, China, 7 June 2015; pp. 251–255. [Google Scholar]
- Lv, N.H. Application of infrared thermal imager in whole process monitoring. China Traffic Inf. Ind. 2009, 5, 85–86. [Google Scholar]
- Yang, L. Principles and Techniques of Infrared Thermography Temperature Measurement; Science Press: Beijing, China, 2012. [Google Scholar]
- Minkina, W.; Dudzik, S. Infrared Thermography: Errors and Uncertainties; Wiley: New York, NY, USA, 2009. [Google Scholar]
- Li, J.; Song, G.; Dong, S.; Chen, W.L.; Wang, H.C. Research progress and trends of uncooled infrared focal plane detectors. Infrared 2020, 41, 1–14, 24. [Google Scholar]
- Dai, S.S. Infrared Focal Plane Array Imaging and Its Non-Uniformity Correction Technique; Science Press: Beijing, China, 2015. [Google Scholar]
- Xing, S.X. Infrared Thermal Imaging and Signal Processing; National Defense Industry Press: Beijing, China, 2011. [Google Scholar]
- Chen, R.; Tan, X. Study on non-uniformity correction of infrared image. Infrared Technol. 2002, 24, 1–3. [Google Scholar]
- Scribner, D.A.; Sarkady, K.A.; Caulfield, J.T. Nonuniformity correction for staring IR focal plane arrays using scene-based techniques. Infrared Detect. Focal Plane Arrays Int. Soc. Opt. Photonics 1990, 1308, 224–233. [Google Scholar]
- Qian, W.; Qian, C.; Gu, G. Space low-pass and temporal high-pass nonuniformity correction algorithm. Opt. Rev. 2010, 17, 24–29. [Google Scholar] [CrossRef]
- Harris, J.G.; Chiang, Y.M. Nonuniformity correction of infrared image sequences using the constant-statistics constraint. IEEE Trans. Process. 1999, 8, 1148–1151. [Google Scholar] [CrossRef] [PubMed]
- Torres, F.; Torres, S.N.; Martín, C.S. A recursive least square adaptive filter for nonuniformity correction of infrared image sequences. In Proceedings of the Iberoamerican Congress on Pattern Recognition, Havana, Cuba, 15–18 November 2005; pp. 540–546. [Google Scholar]
- Jiang, G.; Jia, J.; Liu, S. Nonuniformity correction of infrared image based on scene matching. Multispectral and Hyperspectral Image Acquisition and Processing. Int. Soc. Opt. Photonics 2001, 4548, 280–283. [Google Scholar]
- Bai, L. Research on Non-Uniformity Correction Method of Infrared Images with Adaptation to Integration Time Adjustment; University of Chinese Academy of Sciences (Institute of Optoelectronics Technology, Chinese Academy of Sciences): Beijing, China, 2020. [Google Scholar] [CrossRef]
- Yang, Z.W. Research on Non-Uniformity Correction Technology of Hyperspectral Remote Sensing Images Based on CMOS Sensors; University of Chinese Academy of Sciences (Changchun Institute of Optical Precision Machinery and Physics, Chinese Academy of Sciences): Beijing, China, 2020. [Google Scholar] [CrossRef]
- Huang, Y.; Zhang, B.H.; Wu, J.; Ji, L.; Wu, X.D.; Yu, S.K. Adaptive multipoint calibration non-uniformity correction algorithm. Infrared Technol. 2020, 42, 637–643. [Google Scholar] [CrossRef]
- Wang, J.; Hong, W.Q.; Ge, P.; Wang, X.D.; Pan, C. An improved method for non-uniformity correction of infrared images based on pixel-level radiometric self-calibration. Infrared Technol. 2021, 43, 246–250. [Google Scholar]
- Chen, Q.; Sui, X.B. Infrared Image Processing Theory and Technology; Electronic Industry Press: Beijing, China, 2018. [Google Scholar]
- Wen, G.J.; Wang, H.M.; Zhong, C.; Shang, Z.M. A preferential method for parameterized correction of infrared nonuniformity based on image entropy. Space Return Remote Sens. 2021, 42, 91–98. [Google Scholar]
- Donoho, D.L.; Johnstone, I.M.; Kerkyacharian, G. Universal Near Minimaxity of Wavelet Shrinkage; Springer: New York, NY, USA, 1997. [Google Scholar]
- Mihcak, M.K.; Kozintsev, I.; Ramchandran, K. Low-complexity image denoising based on statistical modeling of wavelet coefficients. IEEE Signal Process. Lett. 1999, 6, 300–303. [Google Scholar] [CrossRef]
- Zhang, Y.; Wang, X.Q.; Peng, Y.N. Improved mean filtering algorithm with adaptive central weighting. J. Tsinghua Univ. Nat. Sci. Ed. 1999, 39, 3. [Google Scholar]
- Zhang, C.J.; Fu, M.Y.; Jin, M. Infrared image denoising method based on discrete orthogonal wavelet transform. Infrared Laser Eng. 2003, 32, 6. [Google Scholar]
- Buades, A.; Coll, B.; Morel, J.M. A non-local algorithm for image denoising. In Proceedings of the Computer Vision and Pattern Recognition, San Diego, CA, USA, 20–25 June 2005. [Google Scholar]
- Dabov, K.; Foi, A.; Katkovnik, V. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering. IEEE Trans. Image Process. 2007, 16, 2080–2095. [Google Scholar] [CrossRef] [PubMed]
- Chen, S.; Li, Y.J.; Di, C. An Infrared Image Denoising Algorithm Based on Wavelet Information Redundancy. Chinese Patent CN103400358A, 20 November 2013. [Google Scholar]
- Dai, Y.; Zhu, D.; Wu, D.H. Shock search particle swarm optimization algorithm based on kernel matrix synergistic evolution. J. Chongqing Univ. Posts Telecommun. (Nat. Sci. Ed.) 2016, 28, 247–253. [Google Scholar]
- Divakar, N.; Babu, R.V. Image Denoising via CNNs: An Adversarial Approach. In Proceedings of the Computer Vision & Pattern Recognition Workshops, Honolulu, HI, USA, 21–26 July 2017; pp. 1076–1083. [Google Scholar]
- Zhang, F.; Cai, N.; Wu, J. Image denoising method based on a deep convolution neural network. IET Image Process. 2018, 12, 485–493. [Google Scholar] [CrossRef]
- Liu, X. Research on Infrared Image Denoising Algorithm; Xi’an University of Electronic Science and Technology: Xi’an, China, 2019. [Google Scholar] [CrossRef]
- Xu, J.W.; Han, J.; Ding, L.H. Improved compressed-aware infrared image denoising algorithm. Electron. Meas. Technol. 2021, 44, 107–111. [Google Scholar] [CrossRef]
- Mao, X.J.; Shen, C.; Yang, Y.B. Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections. arXiv 2016, arXiv:1603.09056. [Google Scholar]
- Zhang, K.; Zuo, W.; Chen, Y. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. IEEE Trans. Image Process. 2017, 26, 3142–3155. [Google Scholar] [CrossRef]
- Liu, C.; Shang, C.; Qin, A. A Multi-Scale Image Denoising Algorithm Based on Inflated Residual Convolutional Networks; Springer: Singapore, 2019. [Google Scholar]
- Lin, H.W.; Chen, J.R.; Niu, Y.Z. A multi-stage image denoising method based on recurrent neural networks. Small Microcomput. Syst. 2022, 13, 1–9. Available online: http://kns.cnki.net/kcms/detail/21.1106.TP.20210818.1138.040.html (accessed on 6 July 2022).
- Munteanu, C.; Rosa, A. Gray-scale image enhancement as an automatic process driven by evolution. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 2004, 34, 1292–1298. [Google Scholar] [CrossRef] [PubMed]
- Wang, Z.J.; Luo, Y.Y.; Jiang, S.Z.; Xiong, N.F.; Wan, L.T. An improved algorithm for adaptive infrared image enhancement based on guided filtering. Spectrosc. Spectr. Anal. 2020, 40, 5. [Google Scholar]
- Yu, T.H.; Dai, J.M. A new technique for infrared image enhancement combining the visual properties of human eyes. Infrared Laser Eng. 2008, 37, 16–19. [Google Scholar]
- Dai, S.S.; Xu, H.; Liu, Q. Infrared image enhancement algorithm based on the visual characteristics of human eyes. Semicond. Optoelectron. 2016, 1, 4. [Google Scholar]
- Jia, Q.; Lu, X.L.; Wu, C. Research on infrared image enhancement based on the visual characteristics of human eyes. Infrared Technol. 2010, 32, 5. [Google Scholar]
- Zhai, H.X.; He, J.Q.; Wang, Z.J.; Jing, J.B.; Chen, W.Z. Improved Retinex and Multi-Image Fusion Algorithm for Low Illumination Image Enhancement. Infrared Technol. 2021, 43, 987–993. [Google Scholar]
- Xie, F.Y.; Tang, M.; Zhang, R. A review of Retinex-based image enhancement methods. Data Acquis. Process. 2019, 34, 1–11. [Google Scholar] [CrossRef]
- Boyd, L. Analysis of infrared thermography data for icing applications. In Proceedings of the 29th Aerospace Sciences Meeting, Reno, NV, USA, 7–10 January 1991. [Google Scholar]
- Yang, L. Infrared thermography temperature calculation and error analysis. Infrared Technol. 1999, 21, 5. [Google Scholar]
- Yang, L.; Kou, W.; Liu, H.K. Measurement of surface emissivity by thermal imaging cameras and error analysis. Laser Infrared 2002, 32, 3. [Google Scholar]
- Kou, W.; Yang, L. Analysis of the influencing factors of errors in thermal measurements. Infrared Technol. 2001, 23, 32–34. [Google Scholar]
- Liu, H.K.; Yang, L. Influence of solar radiation on the temperature measurement error of infrared thermal imaging cameras. Infrared Technol. 2002, 24, 34–37. [Google Scholar]
- Zhang, J.; Yang, L.; Liu, H.K. Influence of high ambient temperature objects on the temperature measurement error of thermal imaging cameras. Infrared Technol. 2005, 27, 419–422. [Google Scholar]
- Pokorni, S. Error Analysis of Surface Temperature Measurement by Infrared Sensor. Int. J. Infrared Millim. Waves 2004, 25, 1523–1533. [Google Scholar] [CrossRef]
- Xu, Y.H.; Wu, M.; Cao, W.H.; Ning, Z.Y. Infrared image recognition detection method for blast furnace temperature field and its application. Control Eng. 2005, 12, 354–356. [Google Scholar]
- Fu, L.; Wang, Z.P.; Liu, X.W.; Du, S.G. Infrared thermographic detection of surface temperature distribution in the inertial friction welding zone. J. Weld. 1999, S1, 48–53. [Google Scholar]
- Cai, L.J.; Zhou, K.L.; Shen, G.Z. High-precision temperature calibration technology for infrared thermal imaging cameras. Infrared Laser Eng. 2021, 50, 8. [Google Scholar]
- Elmahdy, A.H.; Devine, F. Laboratory Infrared Thermography Technique for Window Surface Temperature Measurements. Ashrae Trans. 2005, 111, 561–571. [Google Scholar]
- Inagaki, T.; Okamoto, Y. Surface temperature measurement near ambient conditions using infrared radiometers with different detection wavelength bands by applying a grey- body approximation: Estimation of radiative properties for non-metal surfaces-ScienceDirect. NDT E Int. 1996, 29, 363–369. [Google Scholar] [CrossRef]
- Inagaki, T.; Okamoto, Y. Surface Temperature Measurement Using Infrared Radiometer by Applying a Pseudo-Gray-Body Approximation: Estimation of Radiative Property for Metal Surface. ASME J. Heat Transf. 1996, 118, 73–78. [Google Scholar] [CrossRef]
- Gaussorgues, G. Infrared Thermography; Springer: Dordrecht, The Netherlands, 1994. [Google Scholar]
- Okamoto, Y.; Inagaki, T.; Sekiya, M. Surface Temperature Measurement Using Infrared Radiometer. 1st Report. Radiosity Coefficient and Radiation Temperature. Trans. Jpn. Soc. Mech. Eng. 1993, 59, 3932–3937. [Google Scholar] [CrossRef] [Green Version]
- Du, Y.X.; Hu, Z.Q.; Ge, Y.H. Effect of distance on infrared temperature measurement with different intensity heat sources and compensation. Infrared Technol. 2019, 41, 6. [Google Scholar]
- Zhang, Z.Q.; Wang, P.; Zhao, S.J. Analysis of the influence of target distance and angle on the temperature measurement accuracy of infrared thermal imager. J. Tianjin Univ. Nat. Sci. Eng. Technol. Ed. 2021, 54, 8. [Google Scholar]
- Chen, P.F.; Hu, Y.B. Improving the measurement accuracy of infrared pyrometer. Petrochem. Appl. 2006, 2, 45–47. [Google Scholar]
- Zhang, R. Deep Learning-Based Infrared Target Detection and Recognition; University of Chinese Academy of Sciences (Institute of Optoelectronics Technology, Chinese Academy of Sciences): Beijing, China, 2021. [Google Scholar] [CrossRef]
- Uijlings, J.R.R.; Sande, K.; Gevers, T. Selective Search for Object Recognition. Int. J. Comput. Vis. 2013, 104, 154–171. [Google Scholar] [CrossRef]
- Zitnick, C.L.; Dollár, P. Edge boxes: Locating object proposals from edges. In European Conference on Computer Vision; Springer: Cham, Switzerland, 2014; pp. 391–405. [Google Scholar]
- Freund, Y.; Schapire, R.E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 1997, 55, 119–139. [Google Scholar] [CrossRef]
- Nilsson, R.; Pena, J.M.; Björkegren, J. Evaluating feature selection for SVMs in high dimensions. In Proceedings of the European Conference on Machine Learning, Berlin, Germany, 18–22 September 2006; pp. 719–726. [Google Scholar]
- Redmon, J.; Divvala, S.; Girshick, R. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 779–788. [Google Scholar]
- Redmon, J.; Farhadi, A. YOLO9000: Better, Faster, Stronger. In Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 6517–6525. [Google Scholar]
- Redmon, J.; Farhadi, A. Yolov3: An incremental improvement. arXiv 2018, arXiv:1804.02767. [Google Scholar]
- Bai, C.; Wang, Y.J.; Yang, Y.; Djukanovic, M. Lightweight target detection algorithm based on multi-way feature pyramid. Liq. Cryst. Disp. 2021, 36, 1516–1524. [Google Scholar] [CrossRef]
- Feng, Z.Q.; Xie, Z.J.; Bao, Z.W.; Chen, K.W. Real time dense small target detection algorithm for UAV Based on improved yolov5. Acta Aeronaut. Sin. 2022, 1–15. Available online: http://kns.cnki.net/kcms/detail/11.1929.V.20220509.2316.010.html (accessed on 16 June 2022).
- Bochkovskiy, A.; Wang, C.Y.; Liao, H.Y.M. Yolov4: Optimal speed and accuracy of object detection. arXiv 2020, arXiv:2004.10934. [Google Scholar]
- Shi, J.T.; Zhang, G.Q.; Tao, J.; Wu, L.H. Improved pedestrian detection algorithm for YOLOv4 infrared images. Intell. Comput. Appl. 2021, 11, 31–34 + 41. [Google Scholar]
- Lan, X.; Guo, Z.H.; Li, C.G. Aircraft target detection based on attention and feature fusion for optical remote sensing images. Liq. Cryst. Disp. 2021, 36, 1506–1515. [Google Scholar] [CrossRef]
- Zhu, J.; Wang, J.; Wang, Z.; Wang, B. An improved lightweight mask detection algorithm based on YOLOv4-tiny. Liq. Cryst. Disp. 2021, 36, 1525–1534. [Google Scholar] [CrossRef]
- Ding, C.; Jin, K.; Wang, S.X.; Mu, Q.Q.; Xuan, L.; Li, D.Y. Image processing and projection annotation techniques for infrared thermal wave detection of composite materials. Liq. Cryst. Disp. 2021, 36, 1545–1553. [Google Scholar] [CrossRef]
- Li, Z.Z.; Wang, D.M.; Liu, D.C. Progress in hyperspectral remote sensing technology and resource exploration and application. Earth Sci. J. China Univ. Geosci. 2015, 40, 1287–1294. [Google Scholar]
- Chen, W.T.; Zhang, Z.; Wang, Y.X. Research Progress on Mine Development and Remote Sensing Detection of Mine Environment. Remote Sens. Land Resour. 2009, 2, 1–8. [Google Scholar]
- Kruse, F.A. The Effects of Spatial Resolution, Spectral Resolution, and SNR on Geologic Mapping Using Hyperspectral Data, Northern Grapevine Mountains, Nevada. Fac. Publ. 2000, 10, 127601156. [Google Scholar]
- Tong, Q.X. The present and future of hyperspectral remote sensing. J. Remote Sens. 2003, 7, 1–12. [Google Scholar]
- FLAASH Module User’s Guide. Research Systems, Znc, 24 July 2005.
- Xiong, Z.; Tong, Q.X.; Zhang, L.F. A High-Order Neural Network Algorithm for Hyperspectral Remote Sensing Image Classification. Chin. J. Image Graph. 2000, 5, 20–25. [Google Scholar]
- Zhang, S.M.; Jing, F. Temperature and emissivity separation and mineral mapping based on airborne TASI hyperspectral thermal infrared data. Int. J. Appl. Earth Obs. Geoinf. 2015, 40, 19–28. [Google Scholar] [CrossRef]
- Rutkowski, P.; Kastek, M. Detection of the Chemical Agents Based on Hyperspectral Data Analysis. Meas. Autom. Monit. 2019, 1, 65. [Google Scholar]
- Huo, H.Y.; Li, Z.L.; Xing, Z.F. Temperature/emissivity separation using hyperspectral thermal infrared imagery and its potential for detecting the water content of plants. Int. J. Remote Sens. 2018, 40, 1672–1692. [Google Scholar] [CrossRef]
- Wang, X.H.; Tang, B.H.; Zhao, L.L.; Zhang, R.H. A New Method for Temperature/Emissivity Separation from Hyperspectral Thermal Infrared Data. In Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, Cape Town, South Africa, 12–17 July 2009. [Google Scholar]
- Kirkland, L.; Herr, K.; Keim, E. First use of an airborne thermal infrared hyperspectral scanner for compositional mapping. Remote Sens. Environ. 2002, 80, 447–459. [Google Scholar] [CrossRef] [Green Version]
- Black, M.; Riley, T.R. Automated lithological mapping using airborne hyperspectral thermal infrared data: A case study from Anchorage Island, Antarctica. Remote Sens. Environ. 2016, 176, 225–241. [Google Scholar] [CrossRef]
- Martin, S.; Gilles, R.; Philippe, L. A Hyperspectral Thermal Infrared Imaging Instrument for Natural Resources Applications. Remote Sens. 2012, 4, 3995–4009. [Google Scholar]
- Gerhards, M.; Schlerf, M.; Mallick, K. Challenges and future perspectives of multi-/Hyperspectral thermal infrared remote sensing for crop water-stress detection: A review. Remote Sens. 2019, 11, 1240. [Google Scholar] [CrossRef]
- Wang, N.; Wu, H. Temperature and Emissivity Retrievals From Hyperspectral Thermal Infrared Data Using Linear Spectral Emissivity Constraint. IEEE Trans. Geosci. Remote Sens. 2011, 49, 1291–1303. [Google Scholar] [CrossRef]
- Riley, D.N.; Hecker, C.A. Mineral Mapping with Airborne Hyperspectral Thermal Infrared Remote Sensing at Cuprite, Nevada, USA. In Thermal Infrared Remote Sensing; Springer: Dordrecht, The Netherlands, 2013. [Google Scholar]
- Wang, Z. Overview of the application of infrared thermometry in China. Laser Infrared 1990, 20, 32–37. [Google Scholar]
- Lv, S.G.; Yang, L.; Yang, Q. Research on the applications of infrared technique in the diagnosis and prediction of diesel engine exhaust fault. J. Therm. Sci. Engl. 2011, 20, 189–194. [Google Scholar] [CrossRef]
- Li, D.G. Infrared technology in automotive applications. Infrared 2011, 32, 40–45. [Google Scholar]
- Ren, H.; Wang, Y.; Xiao, G. Design and experimental study of an active early warning system for bridge anti-ship installations. Laser Infrared 2013, 43, 66–70. [Google Scholar]
- Li, Y.; Xiao, G. Design and study of a foreign body detection system for airport runways. Laser Infrared 2011, 41, 7. [Google Scholar]
- Lahiri, B.B.; Bagavathiappan, S.; Jayakumar, T. Medical applications of infrared thermography: A review. Infrared Phys. Technol. 2012, 55, 221–235. [Google Scholar] [CrossRef] [PubMed]
- Otsuka, M.; Funakubo, F.; Suzuki, T. Real-time monitoring of tablet surface temperature during high-speed tableting by infrared thermal imaging. J. Drug Deliv. Sci. Technol. 2021, 68, 102736. [Google Scholar] [CrossRef]
- Magalhaes, C.; Vardasca, R.; Mendes, J. Recent use of medical infrared thermography in skin neoplasms. Ski. Res. Technol. 2018, 24, 587–591. [Google Scholar] [CrossRef] [PubMed]
- Hu, W.; Yu, B.; Luo, J. Hybrid refractive/diffractive optical system design for light and compact uncooled longwave infrared imager. In Proceedings of the 6th International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT 2012), Xiamen, China, 26–29 April 2012. [Google Scholar] [CrossRef]
- Ding, R.L.; Han, C.Z.; Xie, B.R.; Wang, Y.; Zhang, Z. Infrared remote sensing image ship target detection. Infrared Technol. 2019, 41, 127–133. [Google Scholar]
- Li, J.D. Satellite Remote Sensing Technology (Upper Book); Beijing University of Technology Press: Beijing, China, 2018. [Google Scholar]
- Zheng, X.; Li, J.M.; Lan, L.G.; Zhou, H.P.; Zhu, F.Y. Experimental study of infrared thermal imaging during compression fatigue of PBX. J. Pyrotech. 2009, 32, 18–20 + 36. [Google Scholar]
- Liu, W.; Niu, Y.F.; Xiao, L.L.; Wang, Y.B. Development of infrared focal plane arrays and satellite-based infrared imaging systems. Infrared 2021, 42, 15–24. [Google Scholar]
- Pu, E.P.; Tang, S.L. Application of infrared thermal imaging technology in power system fault diagnosis. Power Technol. 2009, 31, 50–56. [Google Scholar] [CrossRef]
- Zhu, G. Exploration of the application of infrared thermal imaging technology in the condition maintenance of power transmission equipment. Enterp. Technol. Dev. 2015, 34, 41–42. [Google Scholar]
- Li, B.S.; Xu, X.T.; Cui, K.B. Application of Infrared Imaging Technology in Fault Diagnosis of Electrical Equipment. Zhejiang Electr. Power 2014, 401–403, 974–977. [Google Scholar] [CrossRef]
- Zhang, D.; Guo, J.; Yin, G.H. Infrared Thermal Imager In the Distribution Line Fault Diagnosis Application. Equip. Manuf. Technol. 2012, 3, 1–10. [Google Scholar]
- Zhou, R.; Su, H.; Wen, Z. Experimental Study on Leakage Detection of Grassed Earth Dam by Passive Infrared Thermography. ScienceDirect, 4 July 2021. [Google Scholar]
- Huda, A.; Taib, S. Application of infrared thermography for predictive/preventive maintenance of thermal defects in electrical equipment. Appl. Therm. Eng. 2013, 61, 220–227. [Google Scholar] [CrossRef]
- Guo, L.; Xiaoying, M.A.; Shen, J. Application of Infrared Thermal Imager in Electrical Equipment for the Glass Industry. Glass Enamel 2019, 47, 12–16. [Google Scholar]
- Mariprasath, T.; Kirubakaran, V. A real time study on condition monitoring of distribution transformer using thermal imager. Infrared Phys. Technol. 2018, 90, 78–86. [Google Scholar] [CrossRef]
- Geoffrey, O.A. Defect Detection on Electrical Power Equipment Using Thermal Imaging Technology. Master’s Thesis, Universiti Malaysia Pahang, Pekan District, Malaysia, 2013. [Google Scholar]
- Niu, H.; Wang, Y.P.; Zhang, D. Application of infrared thermal imaging technology in 500kV substation energized detec-tion. Power Saf. Technol. 2019, 21, 46–48. [Google Scholar]
- Chen, C.; Wang, Y.Y.; Liang, C.; Xu, J.Y.; Wang, X.J.; He, T.L. Application of infrared thermal imaging technology in power plant transformer operation and maintenance. Sci. Technol. Innov. 2020, 8, 26–27. [Google Scholar]
- Chen, K.X. Infrared thermal imaging technology applied to the diagnosis of defects in high-voltage transmission equipment. Electron. World 2013, 6, 44–45. [Google Scholar]
- Roberts, C.C., Jr. The application of infrared thermography in fire and explosion investigation. Proc. SPIE 1988, 934, 2–9. [Google Scholar]
- Cao, N.Y. Application of Image Recognition Technology in Large Space Building Fire Detection; Anhui University of Technology: Hefei, China, 2008. [Google Scholar]
- Melendez, J.; Castro, A.J.; Lopez, F. Forest fire studies by medium infrared and thermal infrared thermography. Proc. SPIE 2001, 4360, 161–168. [Google Scholar]
- Chen, H.G.; Wang, W.Y.; Xu, A.H. Construction and application of fire reconnaissance and information transmission system for forest aerial firefighting. For. Fire Prev. 2013, 6, 52–54. [Google Scholar]
- Goffin, B.; Banthia, N.; Yonemitsu, N. Use of infrared thermal imaging to detect corrosion of epoxy coated and uncoated rebar in concrete. Constr. Build. Mater. 2020, 263, 120162. [Google Scholar] [CrossRef]
- Gagnon, M.A.; Lagueux, P.; Gagnon, J.P. Airborne Thermal Infrared Hyperspectral Imaging of Buried Objects. In Proceedings of the SPIE Defense & Security, Baltimore, MD, USA, 14 May 2015. [Google Scholar]
- Sobrino, J.A.; Raissouni, N.; Li, Z.L. A Comparative Study of Land Surface Emissivity Retrieval from NOAA Data. Remote Sens. Environ. 2001, 75, 256–266. [Google Scholar] [CrossRef]
- Huo, H.Y.; Jiang, X.G.; Song, X.F. Detection of Coal Fire Dynamics and Propagation Direction from Multi-Temporal Nighttime Landsat SWIR and TIR Data: A Case Study on the Rujigou Coalfield, Northwest (NW) China. Remote Sens. 2014, 6, 1234–1259. [Google Scholar] [CrossRef]
- Abstract Advances in Research Products from Combined CALIPSO-CloudSat Observations within the A-Train Aerosol and Elevated Cloud Optical Depth Direct Retrieval over Ocean. In Proceedings of the 17th Conference on Air Sea Interaction, New York, NY, USA, 26–27 September 2010.
- Li, Z.L.; Tang, B.H.; Wu, H. Satellite-Derived Land Surface Temperature: Current Status and Perspectives. Remote Sens. Environ. 2013, 131, 14–37. [Google Scholar] [CrossRef]
- Manolakis, D.; Pieper, M.; Truslow, E. Longwave infrared hyperspectral imaging: Principles, progress, and challenges. IEEE Geosci. Remote Sens. Mag. 2019, 7, 72–100. [Google Scholar] [CrossRef]
- Rock, G. Plant species discrimination using emissive thermal infrared imaging spectroscopy. Int. J. Appl. Earth Obs. Geoinf. 2016, 53, 16–26. [Google Scholar] [CrossRef]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hou, F.; Zhang, Y.; Zhou, Y.; Zhang, M.; Lv, B.; Wu, J. Review on Infrared Imaging Technology. Sustainability 2022, 14, 11161. https://doi.org/10.3390/su141811161
Hou F, Zhang Y, Zhou Y, Zhang M, Lv B, Wu J. Review on Infrared Imaging Technology. Sustainability. 2022; 14(18):11161. https://doi.org/10.3390/su141811161
Chicago/Turabian StyleHou, Fujin, Yan Zhang, Yong Zhou, Mei Zhang, Bin Lv, and Jianqing Wu. 2022. "Review on Infrared Imaging Technology" Sustainability 14, no. 18: 11161. https://doi.org/10.3390/su141811161