전자통신동향분석 (Electronics and Telecommunications Trends)

한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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초분광 이미징 기술동향

Recent Trends of Hyperspectral Imaging Technology

  • 발행 : 2019.02.01
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⟨ 이전 논문 다음 논문 ⟩

초록

Over the past 30 years, significant developments have been made in hyperspectral imaging (HSI) technologies that can provide end users with rich spectral, spatial, and temporal information. Owing to the advances in miniaturization, cost reduction, real-time processing, and analytical methods, HSI technologies have a wide range of applications from remote-sensing to healthcare, military, and the environment. In this study, we focus on the latest trends of HSI technologies, analytical methods, and their applications. In particular, improved machine learning techniques, such as deep learning, allows the full use of HSI technologies in classification, clustering, and spectral mixture algorithms. Finally, we describe the status of HSI technology development for skin diagnostics.

키워드

HJTOCM_2019_v34n1_86_f0001.png 이미지

(그림 1) 초분광 이미징 데이터 습득형태 (a) Point- scan (b) Line-scan (c) Area-scan (d) snapshot

HJTOCM_2019_v34n1_86_f0002.png 이미지

(그림 2) 초분광 이미징 시스템의 예

HJTOCM_2019_v34n1_86_f0003.png 이미지

(그림 3) 초분광 이미징 분석절차

HJTOCM_2019_v34n1_86_f0004.png 이미지

(그림 4) 토지의 Fe과 Zn의 분포도

HJTOCM_2019_v34n1_86_f0005.png 이미지

(그림 5) HSI를 이용하여 분류한 사과의 상태 분류

<표 1> 초분광 이미징 사용대역

HJTOCM_2019_v34n1_86_t0001.png 이미지

<표 2> 밴드수에 따른 분광 기술

HJTOCM_2019_v34n1_86_t0002.png 이미지

참고문헌

  1. A.F.H. Goetz et al., "Imaging Spectrometry for Earth Remote Sensing," Sci., vol. 228, no. 4704, 1985, pp. 1147-1153. https://doi.org/10.1126/science.228.4704.1147
  2. A.F.H. Goetz, "Three Decades of Hyperspectral Remote Sensing of the Earth: a Personal View," Remote Sens. Environment, vol. 113, no. 1, 2009, pp. S5-S16. https://doi.org/10.1016/j.rse.2007.12.014
  3. Technavio, "Global Hyperspectral Imaging Market(2016-2020)," Technavio, 2016.
  4. G. Lu and B. Fei. "Medical Hyperspectral Imaging: a Review," J. Biomedical Opt., vol. 19, no. 1, 2014, pp. 010901:1-010901:24.
  5. J.R. Gilchrist, "Hyperspectral Imaging Spectroscopy: A Look at RealLife Applications," Phtonics Media, 2018.
  6. R. Hruska et al., "Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle," Remote Sens., vol. 4, no. 9, 2012, pp. 2736-2752. https://doi.org/10.3390/rs4092736
  7. T.W. Sawyer, AS. Luthman, and S.E Bohndiek, "Evaluation of Illumination System Uniformity for Wide-Field Biomedical Hyperspectral Imaging," J. Opt., vol. 19, no. 4, 2017, pp. 045301:1-045301:10.
  8. M. Aikio, "Hyperspectral Prism-Grating-Prism Imaging Spectrograph," in VTT Pubrications 435, Technical Research Centre of Finland, Olul, Finland, 2001.
  9. D.-W. Sun, "Hyperspectral Imaging for Food Quality Analysis and Control," Academic Press Inc., London, UK, 2010.
  10. K. Degraux et al., "Multispectral Compressive Imaging Strategies Using Fabry-Perot Filtered Sensors," arXiv: 1802.02040v1, 2018.
  11. N. Hagen et al., "Review of Snapshot Spectral Imaging Technologies," Opt. Eng., vol. 52, no. 9, 2013, pp. 090901:1-090901:23.
  12. R. Abdlaty et al., "Hyperspectral Imaging: Comparison of Acousto-Optic and Liquid Crystal Tunable Filters," Proc. SPIE, vol. 10573, 2018, pp. 105732P:1-105732P:9.
  13. L. Bei et al., "Acousto-Optic Tunable Filters: Fundamentals and Applications as Applied to Chemical Analysis Techniques," Progress Quantum Electron., vol. 28, no. 2, 2004, pp. 67-87. https://doi.org/10.1016/S0079-6727(03)00083-1
  14. A. Plaza et al. "Recent Advances in Techniques for Hyperspectral Image Processing," Remote Sens. Environment, vol. 113, sup. 1, pp. S110-S122. https://doi.org/10.1016/j.rse.2007.07.028
  15. P. Ghamisi et al., "Advances in Hyperspectral Image and Signal Processing: a Comprehensive Overview of the State of the Art," IEEE Geosci. Remote Sens. Mag., vol. 5, no. 4, 2017, pp. 37-78. https://doi.org/10.1109/MGRS.2017.2762087
  16. R. Heylen, M. Parente, and P. Gader, "A Review of Nonlinear Hyperspectral Unmixing Methods," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, 2014, pp. 1844-1868. https://doi.org/10.1109/JSTARS.2014.2320576
  17. U. B. Gewali1, S. T. Monteiro, and E. Saber, "Machine Learning Based Hyperspectral Image Analysis: A Survey," arXiv: 1802.08701, 2018.
  18. R. Ablin and C. H. Sulochana, "A Survey of Hyperspectral Image Classification in Remote Sensing." Int. J. Adv. Research Comput. Commun. Eng., vol. 2, no. 8, 2013, pp. 2986-3000.
  19. A. Setiyoko, I.G.W.S. Dharma, and T. Haryanto, "Recent Development of Feature Extraction and Classification Multispectral/Hyperspectral Images: A Systematic Literature Review," J. Phys.: Conf. Series, vol. 801, no. 1, 2017, pp. 012045:1-012045:6.
  20. H. Su, Q. Du, and P. Du, "Hyperspectral Image Visualization Using Band Selection," IEEE J. Selected Topics Appli. Earth Observ. Remote Sens., vol. 7, no. 6, 2014, pp. 2647-2658. https://doi.org/10.1109/JSTARS.2013.2272654
  21. S. Sanjith and R. Ganesan, "A Review on Hyperspectral Image Compression," Int. Conf. Contr., Instrument., Commun. Computational Technol. (ICCICCT), Kanyakumari, India, 2014, pp. 1159-1163.
  22. P. Ghamisi et al., "New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning ," IEEE Geosci. Remote Sens. Mag., vol. 6, no. 3, 2018, pp. 10-43. https://doi.org/10.1109/mgrs.2018.2854840
  23. L. He et al., "Recent Advances on Spectral-Spatial Hyperspectral Image Classification: An Overview and New Guidelines," IEEE Trans. Geosci. Rem. Sens., vol. 56, no. 3, 2018, pp. 1579-1597. https://doi.org/10.1109/TGRS.2017.2765364
  24. L. Mou et al., "Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery," 2018. https://arxiv.org/abs/1803.02642. https://doi.org/10.1109/TGRS.2018.2863224
  25. X. Zhu et al., "Deep Learning in Remote Sensing," IEEE Geosci. Remote Sens. Mag., vol. 5, no. 4, 2017, pp. 8-36. https://doi.org/10.1109/MGRS.2017.2762307
  26. X. Zhong et al., "Hyperspectral Unmixing via Deep Convolutional Neural Networks," IEEE Geosci. Remote Sens. Lett., vol. 15, no. 11, 2018, pp. 1-5. https://doi.org/10.1109/LGRS.2017.2781679
  27. Z. He et al., "Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification," Remote Sens., vol. 9, 2017, pp. 1042:1-1042:27.
  28. L. Ziu et al., "Generative Adversarial Networks for Hyperspectral Image Classification," IEEE Trans. Geosci. Remote Sens., vol. 56, no. 9, 2018, pp. 5046-5063. https://doi.org/10.1109/TGRS.2018.2805286
  29. M. J. Khan et al., "Modern Trends in Hyperspectral Image Analysis: A Review," IEEE Access, vol. 6, 2018, pp. 14118-14129. https://doi.org/10.1109/ACCESS.2018.2812999
  30. P. Baeck et al., "High Resolution Vegetation Mapping with a Novel Compact Hyperspectral Camera System," in Int. Soc. Precision Agriculture, St. Louis, MO, USA, 2016, pp. 1-12.
  31. M. Denk et al., "Mapping of Iron and Steelwork By-Products Using Close Range Hyperspectral Imaging: A Case Study in Thuringia, Germany," Eur. J. Remote Sens., vol. 48, no. 1, 2015, pp. 489-509. https://doi.org/10.5721/EuJRS20154828
  32. M. Vohland et al., "Quantification of Soil Properties with Hyperspectral Data: Selecting Spectral Variables with Different Methods to Improve Accuracies and Analyze Prediction Mechanisms," Remote Sens., vol. 9, no. 11, 2017, pp. 1103:1-1103:24.
  33. HySpex, https://www.hyspex.no/hyperspectral_imaging/
  34. J. Transon et al., "Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context," Remote Sens., Vol. 10, no. 2, 2018, pp. 157:1-157:32. https://doi.org/10.3390/rs10101571
  35. R. Zhao et al., "Hyperspectral Anomaly Detection via a Sparsity Score Estimation Framework," IEEE Trans. Geosci. Remote Sens., vol. 55, no. 6, 2017, pp. 3208-3222. https://doi.org/10.1109/TGRS.2017.2664658
  36. M.-A. Gagnon et al., "Airborne Thermal Infrared Hyperspectral Imaging of Buried Objects," Proc. SPIE, vol. 9454, 2015, pp. 94540K:1-94540K:10.
  37. Y. Liu et al., "Hyperspectral Imaging Technique for Evaluating Food Quality and Safety During Various Processes: A Review of Recent Applications," Trens. Food Sci. Technol., vol. 69, 2017, pp. 25-35. https://doi.org/10.1016/j.tifs.2017.08.013
  38. S. Jarolmasjed et al., "Hyperspectral Imaging and Spectrometry-Derived Spectral Features for Bitter Pit Detection in Storage Apples," Sensers, vol. 18, no. 5, 2018, pp. 1561:1-1561:11.
  39. J. Qin et al., "Line-Scan Hyperspectral Imaging Techniques for Food Safety and Quality Applications," Appl. Sci. vol. 7, no. 2, 2017, pp.125:1-125:22. https://doi.org/10.3390/app7121251
  40. M. Puneet, "NIR Hyperspectral Imaging For detection of Nut Contamination," New Food, vol. 18, no. 4, 2015, pp. 30-33.
  41. G. Lu and F. Baowei, "Medical Hyperspectral Imaging: a Review," J. Biomed. Opt., vol. 19, no. 1, 2014, pp. 010901:1-010901:23.
  42. N.R. Abbasi et al., "Early Diagnosis of Cutaneous Melanoma: Revisiting the ABCD Criteria," JAMA, vol. 292, no. 22, 2004, pp. 2771:1-2771:6.
  43. I. A. Bratchenko et al., "In Vivo Hyperspectral Imaging of Skin Malignant and Benign Tumors in Visible Spectrum," J. Biomedical Photon. Eng., vol. 4, no. 1, 2018, pp. 010301:1-010301:8.