Précédent|Prochain

Development of hyperspectral imaging remote sensing technology

  • role： First author第一作者
  • Affiliation：

    CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai 200083, China

  • Email：ynliu@mail.sitp.ac.cn
  • Introduction：刘银年，1971年生，男，研究员，研究方向为高光谱遥感成像技术。E-mail：ynliu@mail.sitp.ac.cn
LIU Yinnian

résumé

Hyperspectral remote sensing technology can acquire an object’s geometric, radiation, and spectral information. This technology is an important technique in Earth observations and is increasingly becoming important in applications of natural resource survey, environment and disaster monitoring, precision agriculture, oceans and costal monitoring, and urban planning. In the past decades, several advanced hyperspectral imaging systems from airborne (e.g., AVIRIS, Hymap, OMIS, and PHI) to spaceborne (e.g., EO-1/Hyperion and PROBA/CHRIS) platforms have been designed, built, and operated globally. On the one hand, airborne hyperspectral imager has been developed into commercial operation stage. Examples of international companies that develop airborne systems are Spectra Vista Corporation of America, Specim of Finland, and ITRES Research of Canada. On the other hand, GF-5/AHSI, which is a pioneer in Chinese spaceborne hyperspectral imager, has first realized wide spectrum, wide swath width, and high detection sensitivity. It marks a new era ever since the appearance of EO-1/Hyperion in 2000.In the future, the outlook for hyperspectral remote sensing technique is as follows:(1) The development of large-scale plane array detector, optical machining detection, and signal processing has improved not only the spectral resolution but also the spatial resolution and swath width of hyperspectral imaging. Hyperspectral imager’s spectrum range will cover from UV to LWIR to obtain more abundant spectral information of ground objects, all-day reflectance, and emission spectral characteristics. In addition, the integrated calibration methods of laboratory, in-orbit, and the Earth, the Sun, the Moon, the cold air, and the stars are becoming increasingly abundant and refined to ensure the application efficiency of hyperspectral imager at higher performance. The hyperspectral imaging technology with super wide width and higher resolution also puts forward higher requirements for the further development of large-scale detectors and large-aperture optics with wide working band range.(2) The development of information, imaging, and optical processing technology has introduced new beam splitting technologies and developed the core beam splitting elements from the mature dispersion and interference type to the diversified direction. Many novel optical splitting schemes, such as Acousto-optic Tunable Filter (AOTF), Liquid Crystal Tunable Filter (LCTF), Linear Variable Filter (LVF), Integrated Stepwise Filter (ISF), Tunable Fabry-Perot Filter (TFPF) and computational spectral imaging system based on compressed sensing, are available at present. These spectroscopic image methods are still in the stage of laboratory experiments. An increasing attention has also been paid to the chip-level hyperspectral spectroscopy, which combines light splitting with photoelectric conversion.(3) With the advances in the “artificial intelligence,” machine learning data process, such as neural network and deep learning, has become a trend with hyperspectral imaging to construct an ‘intelligent’ hyperspectral remote sensing satellite system. This technology will integrate the ability of automatic optimization of onboard load parameters and automatic real-time processing of onboard data and product generation. Meanwhile, the amount of remote sensing data obtained is explosively growing with the increase in resolution and information dimensions of hyperspectral imaging instruments. “Big data” feature is significant. Data transmission is an important issue in successfully using the effective data mining and information extraction and improving the efficiency of data compression in the future.(4) The development of small UAV and micro-nano satellite technology has developed hyperspectral imaging toward a low-cost, flexible, integrated, and real-time technology. At present, the light and small hyperspectral imaging technology based on small UAV is greatly demanded and valuable in the fields of agricultural, forestry diseases and insect pests’ detection, target search, and rescue and relief. Micro-nano satellites have low cost and short development cycle and can conduct complex space remote sensing tasks. The combination of hyperspectral imaging and micro-nano satellite technologies will promote the integration of multi-functional structure and space exploration payload. Lightweight, integrated, and systematized hyperspectral remote sensing with space networking and all-time detection will play an important role in the future. It will provide the possibility for hyperspectral remote sensing satellites to enter the commercial field.Many new principles, schemes, and technologies are being implemented and applied in hyperspectral imaging. The integrated acquisition and processing ability of multiple information is also greatly enhanced. The hyperspectral load is gradually developing in the direction of large field of view, large relative aperture, high resolution, and high quantification. The cost of hyperspectral remote sensing technology will be greatly reduced with its continuous development and maturity. The commercial application of this technology will also be an important direction of future development.

mots-clés

remote sensing;hyperspectral imaging;remote sensing payload;wide spectrum;wide swath width;high resolution

References

  1. 1.
    Accettura A G, Bruno C, Casotto S and Marzari F. 2004. Mission to mars using integrated propulsion concepts: considerations, opportunities, and strategies. Acta Astronautica, 54(7): 471-486
  2. 2.
    Achal S, McFee J E, Ivanco T and Anger C. 2007. A thermal infrared hyperspectral imager (tasi) for buried landmine detection//Proceedings Volume 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII. Orlando, Florida, United States: SPIE: 655316 [DOI: 10.1117/12.720453]
  3. 3.
    Asner G P and Heidebrecht K B. 2003. Imaging spectroscopy for desertification studies: comparing AVIRIS and EO-1 Hyperion in Argentina drylands. IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1283-1296
  4. 4.
    August Y, Vachman C, Rivenson Y and Stern A. 2013. Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains. Applied Optics, 52(10): D46-D54
  5. 5.
    Babey S K and Anger C D. 1993. Compact airborne spectrographic imager (CASI): a progress review//Proceedings Volume 1937, Imaging Spectrometry of the Terrestrial Environment. Orlando, FL, United States: SPIE: 152-163
  6. 6.
    Barnsley M J, Settle J J, Cutter M A, Lobb D R and Teston F. 2004. The PROBA/CHRIS mission: a low-cost smallsat for hyperspectral multiangle observations of the Earth surface and atmosphere. IEEE Transactions on Geoscience and Remote Sensing, 42(7): 1512-1520
  7. 7.
    Bessho K, Date K, Hayashi M, Ikeda A, Imai T, Inoue H, Kumagai Y, Miyakawa T, Murata H, Ohno T, Okuyama A, Oyama R, Sasaki Y, Shimazu Y, Shimoji K, Sumida Y, Suzuki M, Taniguchi H, Tsuchiyama H, Uesawa D, Yokota H and Yoshida R. 2016. An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites. Journal of the Meteorological Society of Japan, 94(2): 151-183
  8. 8.
    Brady D J and Gehm M E. 2006. Compressive imaging spectrometers using coded apertures//Proceedings Volume 6246, Visual Information Processing XV. Orlando (Kissimmee), Florida, United States: SPIE: 62460A [
  9. 9.
    Candes E J and Tao T. 2006. Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Transactions on Information Theory, 52(12): 5406-5425
  10. 10.
    Chen R, Gao C, Wu X W, Zhou S Y, Hua J F and Ding L. 2019. Application of FY-4 atmospheric vertical sounder in weather forecast. Journal of Infrared and Millimeter Waves, 38(3): 285-289
  11. 11.
    Cheng Y B, Zarco-Tejada P J, Riaño D, Rueda C A and Ustin S L. 2006a. Estimating vegetation water content with hyperspectral data for different canopy scenarios: relationships between AVIRIS and MODIS indexes. Remote Sensing of Environment, 105(4): 354-366
  12. 12.
    Cheng Y F, Gamon J A, Fuentes D A, Mao Z Y, Sims D A, Qiu H L, Claudio H, Huete A and Rahman A F. 2006b. A multi-scale analysis of dynamic optical signals in a Southern California chaparral ecosystem: a comparison of field, AVIRIS and MODIS data. Remote Sensing of Environment, 103(3): 369-378
  13. 13.
    Chrien T G, Chovit C and Miller, P J. 1993. Imaging spectrometer using a liquid crystal tunable filter//Proceedings Volume 1937, Imaging Spectrometry of the Terrestrial Environment. Orlando, FL, United States: SPIE: 256-261
  14. 14.
    Cocks T, Jenssen R, Stewart A, Wilson I and Shields T. 1998. The HyMapTM airborne hyperspectral sensor: the system, calibration and performance//Presented at 1st EARSEL Workshop on Imaging Spectroscopy. Zurich: [s.n.]
  15. 15.
    Cong R, Wang Y K, Chen G, Cai Q Y, Zhou S, Jiang L, Liu D Q and Xu B Z. 2019. Methods for suppressing multi-order transmission peaks of tunable Fabry-Perot filter. Acta Optica Sinica, 39(3): 0323003
  16. 16.
    Corson M R, Korwan D R, Lucke R L, Snyder W A and Davis C O. 2008. The hyperspectral imager for the coastal ocean (HICO) on the international space station//Proceedings of 2008 2008 IEEE International Geoscience and Remote Sensing Symposium. Boston, MA: IEEE: IV-101-IV-104
  17. 17.
    Cui T W, Ma Y and Zhang J. 2003. The development and applications of the airborne hyperspectral remote sensing. Remote Sensing Technology and Application, 18(2): 118-122
  18. 18.
    Dong X F, Gan F P, Li N, Yan B K, Zhang L, Zhao J Q, Yu J C, Liu R Y and Ma Y N. 2020. Fine mineral identification of GF-5 hyperspectral image. Journal of Remote Sensing, 24(4): 454-464
  19. 19.
    Duarte M F, Davenport M A, Takhar D, Laska J N, Sun T, Kelly K F and Baraniuk R G. 2008. Single-pixel imaging via compressive sampling. IEEE Signal Processing Magazine, 25(2): 83-91
  20. 20.
    Fabry C and Perot A. 1897. Sur les franges des lames minces argentées et leur application à la mesure de petites épaisseurs d’air. Ann. Chim. Phys., 12: 459-501.
  21. 21.
    Fu A, Zhang H D, Zhang X Y, Sang H S, Zhang T Y and Xie C S. 2013. A tunable liquid crystal Fabry-Perot hyperspectral imaging device in mid-infrared wavelength range//Proceedings Volume 8917, MIPPR 2013: Multispectral Image Acquisition, Processing, and Analysis. Wuhan, China: SPIE: 891704 [DOI: 10.1117/12.2032159]
  22. 22.
    Gendrin A, Mangold N, Bibring J-P, Langevin Y, Gondet B, Poulet F, Bonello G, Quantin C, Mustard J, Arvidson R and LeMouélic S. 2005. Sulfates in Martian layered terrains: the OMEGA/mars express view. Science, 307(5715): 1587-1591
  23. 23.
    Graff D L and Love S P. 2014. Adaptive hyperspectral imaging with a MEMS-based full-frame programmable spectral filter//Proceedings Volume 9101, Next-Generation Spectroscopic Technologies VII. Baltimore, Maryland, United States: SPIE: 910111 [DOI: 10.1117/12.2051436]
  24. 24.
    Green R O, Pieters C, Mouroulis P, Eastwood M, Boardman J, Glavich T, Isaacson P, Annadurai M, Besse S, Barr D, Buratti B, Cate D, Chatterjee A, Clark R, Cheek L, Combe J, Dhingra D, Essandoh V, Geier S, Goswami J N, Green R, Haemmerle V, Head J, Hovland L, Hyman S, Klima R, Koch T, Kramer G, Kumar A S K, Lee K, Lundeen S, Malaret E, McCord T, McLaughlin S, Mustard J, Nettles J, Petro N, Plourde K, Racho C, Rodriquez J, Runyon C, Sellar G, Smith C, Sobel H, Staid M, Sunshine J, Taylor L, Thaisen K, Tompkins S, Tseng H, Vane G, Varanasi P, White M and Wilson D. 2011. The Moon Mineralogy Mapper (M3) imaging spectrometer for lunar science: instrument description, calibration, on‐orbit measurements, science data calibration and on‐orbit validation. Journal of Geophysical Research: Planets, 116(E10): E00G19
  25. 25.
    Gupta N. 2003. Hyperspectral and Polarization Imaging Applications of Acousto-Optic Tunable Filters. Paris: WCU: 345-348.
  26. 26.
    Gupta N, Dahmani R, Gottlieb M S, Denes L J, Kaminsky B and Metes P. 1999. Hyperspectral imaging using acousto-optic tunable filters//Proceedings Volume 3718, Automatic Target Recognition IX. Orlando, FL, United States: SPIE
  27. 27.
    Hofer S, Kaufmann H J, Stuffler t, Penné B, Schreier G, Müller A, Eckardt A, Bach H, Benz U C and Haydn R. 2007. EnMAP Hyperspectral Imager: an advanced optical payload for future applications in Earth observation programs//Proceedings Volume 6366, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI. Stockholm, Sweden: SPIE: 115-120
  28. 28.
    Hörig B, Kühn F, Oschütz F and Lehmann F. 2001. Hymap hyperspectral remote sensing to detect hydrocarbons. International Journal of Remote Sensing, 22(8): 1413-1422
  29. 29.
    Hu B L, Sun D X and Liu Y N. 2018a. A novel method to remove fringes for dispersive hyperspectral VNIR imagers using back-illuminated CCDs. Remote Sensing, 10(1): 79
  30. 30.
    Hu B L, Zhang J, Cao K Q, Hao S J, Sun D X and Liu Y N. 2018b. Research on the etalon effect in dispersive hyperspectral VNIR imagers using back-illuminated CCDs. IEEE Transactions on Geoscience and Remote Sensing, 56(9): 5481-5494
  31. 31.
    Itten K I, Dell’Endice F, Hueni A, Kneubühler M, Schläpfer D, Odermatt D, Seidel F, Huber S, Schopfer J, Kellenberger T, Bühler Y, D’Odorico P, Nieke J, Alberti E and Meuleman K. 2008. APEX - the hyperspectral ESA airborne prism experiment. Sensors, 8(10): 6235-6259
  32. 32.
    Ji Y. 2006. Development of near space vehicle. International Aviation, (9): 84-85
  33. 33.
    Kittle D, Choi K, Wagadarikar A and Brady D J. 2010. Multiframe image estimation for coded aperture snapshot spectral imagers. Applied Optics, 49(36): 6824-6833
  34. 34.
    Korablev O, Bertaux J L, Fedorova A, Fonteyn D, Stepanov A, Kalinnikov Y, Kiselev A, Grigoriev A, Jegoulev V, Perrier S, Dimarellis E, Dubois J P, Reberac A, Van Ransbeeck E, Gondet B, Montmessin F and Rodin A. 2006. SPICAM IR acousto-optic spectrometer experiment on Mars Express. Journal of Geophysical Research: Planets, 111(E9): E09S03
  35. 35.
    Korablev O, Fedorova A, Bertaux J L, Stepanov A V, Kiselev A, Kalinnikov Y K, Titov A Y, Montmessin F, Dubois J P, Villard E, Sarago V, Belyaev D, Reberac A and Neefs E. 2012. SPICAV IR acousto-optic spectrometer experiment on Venus Express. Planetary and Space Science, 65(1): 38-57
  36. 36.
    Krishna G, Sahoo R N, Pradhan S, Ahmad T and Sahoo P M. 2018. Hyperspectral satellite data analysis for pure pixels extraction and evaluation of advanced classifier algorithms for LULC classification. Earth Science Informatics, 11(2): 159-170
  37. 37.
    Krutz D, Venus H, Eckardt A, Walter I, Sebastian I, Reulke R, Günther B, Zender B, Arloth S, Williges C, Lieder M, Neidhardt M, Grote U, Schrandt F and Wojtkowiak A. 2018. DESIS - DLR earth sensing imaging spectrometer//Satoh S, eds. Image and Video Technology. Wuhan: Springer: 356-368
  38. 38.
    Labate D, Ceccherini M, Cisbani A, De Cosmo V, Galeazzi C, Giunti L, Melozzi M, Pieraccini S and Stagi M. 2009. The PRISMA payload optomechanical design, a high performance instrument for a new hyperspectral mission. Acta Astronautica, 65(9/10): 1429-1436
  39. 39.
    Li D Z. 2014. Radar Coincidence Imaging Technique Research. Changsha: National University of Defense Technology. (李东泽. 2014. 雷达关联成像技术研究. 长沙: 国防科学技术大学)
  40. 40.
    Li X Y, Fan H S, Pan S L, Jiang X H and Wu J Q. 2019. Zhuhai-1 Hyperspectral satellite data and applications. Satellite Application, (8): 12-18
  41. 41.
    Li Z Z, Yang R H, Dang F X, Du P J, Zhang X F, Tan B X, Zhao H J and Su H J. 2012. A review on the geological applications of hyperspectral remote sensing technology//Proceedings of the 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Shanghai: IEEE: 1-4
  42. 42.
    Liu W J, Hu C Z, Jiang P P, Wu B and Shen Y H. 2015. Ultra-high-resolution spectrometry incorporating two-dimension dispersing spectrometer and tunable fabry-perot filter. Acta Optica Sinica, 35(6): 063007
  43. 43.
    Liu Y, Yuan X, Suo J L, Brady D J and Dai Q H. 2019a. Rank minimization for snapshot compressive imaging. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(12): 2990-3006
  44. 44.
    Liu Y N, Ding X Z and Li Z Z. 2015. Specification design of hyperspectral imaging remote sensor used in geosciences. Earth Science-Journal of China University of Geosciences, 40(8): 1295-1300
  45. 45.
    Liu Y N, Sun D X, Cao K Q, Liu S F, Chai M Y, Liang J and Yuan J. 2020a. Evaluation of GF-5 AHSI on-orbit instrument radiometric performance. Journal of Remote Sensing, 24(4): 352-359
  46. 46.
    Liu Y N, Sun D X, Hu X N, Liu S F, Cao K Q, Chai M Y, Liao Q J, Zuo Z Q, Hao Z Y, Duan W B, Zhou W Y N, Zhang J and Zhang Y. 2020b. Development of visible and short-wave infrared hyperspectral imager onboard GF-5 satellite. Journal of Remote Sensing, 24(4): 333-344
  47. 47.
    Liu Y N, Sun D X, Hu X N, Liu S H, Gan F P and Ge S L. 2019. Observation capability and application prospect of the advanced hyperspectral imager. Areospace Shanghai, 36(S2): 99-105
  48. 48.
    Liu Y N, Sun D X, Hu X N, Ye X, Li Y D, Liu S F, Cao K Q, Chai M Y, Zhou W Y N, Zhang J, Zhang Y, Sun W W and Jiao L L. 2019b. The advanced hyperspectral imager: aboard China's gaofen-5 satellite. IEEE Geoscience and Remote Sensing Magazine, 7(4): 23-32
  49. 49.
    Liu Y N, Xue Y Q, Wang J Y and Shen M M. 2002. Operational modular imaging spectrometer. Journal of Infrared and Millimeter Waves, 21(1): 9-13
  50. 50.
    Loizzo R, Guarini R, Longo F, Scopa T, Formaro R, Facchinetti C and Varacalli G. 2018. Prisma: the Italian hyperspectral mission//Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia: IEEE: 175-178
  51. 51.
    Love S P. 2009. Programmable matched filter and Hadamard transform hyperspectral imagers based on micromirror arrays. Proc//Proceedings Volume 7210, Emerging Digital Micromirror Device Based Systems and Applications. San Jose, California, United States: SPIE: 721007 [DOI: 10.1117/12.808060]
  52. 52.
    Love S P and Graff D L. 2014. Full-frame programmable spectral filters based on micromirror arrays. Journal of Micro/Nanolithography, MEMS, and MOEMS, 13(1): 011108
  53. 53.
    Lu Y F, Cai Z J, Guo P L, Su Y F and Wu J H. 2018. An automatic cavity-length stabilizing and monitoring system for tunable Fabry-Perot filter//Proceedings Volume 10827, Sixth International Conference on Optical and Photonic Engineering (icOPEN 2018).
  54. 54.
    Lucey P G, Horton K A, Williams T J, Hinck K, Budney C, Rafert B and Rusk T B. 1993. SMIFTS: a cryogenically cooled, spatially modulated imaging infrared interferometer spectrometer//Proceedings Volume 1937, Imaging Spectrometry of the Terrestrial Environment. Orlando, FL, United States: SPIE: 129-141
  55. 55.
    Ma G X, Xue Y Q and Li G F. 2008. Satellite remote sensing for haze monitoring in Pearl River delta region. Science and Technology Review, 26(16): 72-76
  56. 56.
    Ma Y P, Zhang W and Liu D X. 2012. Characteristics of hyperspectral reconnaissance and threat to ground military targets. Aerospace Shanghai, 29(1): 37-40, 59
  57. 57.
    Maestro M M, Paringit E C, Viray F A, Calvo J S, Ishida T, Takahashi Y, Kurihara J, Vergel K and Magallon B P. 2016. Determina on of the pre-launch image- processing techniques for Liquid Crystal Tunable Filter (LCTF) for PHL-Microsat DIWATA-1//Proceedings of the 37th Asian Conference on Remote Sensing. Colombo, Sri Lanka
  58. 58.
    Mahmoud K, Park S and Lee D H. 2018. Development of a new linearly variable edge filter (LVEF)-based compact slit-less mini-spectrometer. Journal of Physics: Conference Series, 972: 012026
  59. 59.
    Makisara K, Meinander M, Rantasuo M, Okkonen J, Aikio M and Sipola K. 1993. Airborne imaging spectrometer for applications (AISA)//Proceedings of IGARSS '93-IEEE International Geoscience and Remote Sensing Symposium. Tokyo: IEEE: 479-481
  60. 60.
    Mauser W. 2003. The airborne visible/infrared imaging spectrometer AVIS-2-multiangular und hyperspectral data for environmental analysis//Proceedings of 2003 IEEE International Geoscience and Remote Sensing Symposium. Toulouse: IEEE: 2020-2022
  61. 61.
    Michael S. 2006. Fixed-wing unmanned aircraft are air force's best near-space option. Defense Daily, 229(30)
  62. 62.
    Michel S, Gamet P and Lefevre-Fonollosa M J. 2011. HYPXIM — A hyperspectral satellite defined for science, security and defence users//Proceedings of the 3rd Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Lisbon, Portugal: IEEE: 1-4
  63. 63.
    Murchie S, Arvidson R, Bedini P, Beisser K, Bibring J P, Bishop J, Boldt J, Cavender P, Choo T, Clancy R T, Darlington E H, Des Marais D, Espiritu R, Fort D, Green R, Guinness E, Hayes J, Hash C, Heffernan K, Hemmler J, Heyler G, Humm D, Hutcheson J, Izenberg N, Lee R, Lees J, Lohr D, Malaret E, Martin T, McGovern J A, McGuire P, Morris R, Mustard J, Pelkey S, Rhodes E, Robinson M, Roush T, Schaefer E, Seagrave G, Seelos F, Silverglate P, Slavney S, Smith M, Shyong W J, Strohbehn K, Taylor H, Thompson P, Tossman B, Wirzburger M and Wolff M. 2007. Compact reconnaissance imaging spectrometer for mars (CRISM) on mars reconnaissance orbiter (MRO). Journal of Geophysical Research: Planets, 112(E5): E05S03
  64. 64.
    Natale V G, Kafri A, Tidhar G A, Chen M, Feingersh T, Sagi E, Cisbani A, Baroni M, Labate D, Nadler R, Leizer A, Signorile S, Tempesta P, Magliozzi M L, Catallo C and Pietropaolo A. 2013. SHALOM — Space-borne hyperspectral applicative land and ocean mission//Proceedings of the 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Gainesville, FL: IEEE: 1-4
  65. 65.
    Nink S, Hill J, Buddenbaum H, Stoffels J, Sachtleber T and Langshausen J. 2015. Assessing the suitability of future multi- and hyperspectral satellite systems for mapping the spatial distribution of norway spruce timber volume. Remote Sensing, 7(9): 12009-12040
  66. 66.
    Peón J, Recondo C, Fernández S, Calleja F, De Miguel E and Carretero L. 2017. Prediction of topsoil organic carbon using airborne and satellite hyperspectral imagery. Remote Sensing, 9(12): 1211
  67. 67.
    Pearlman J S, Barry P S, Segal C C, Shepanski J, Beiso D and Carman S L. 2003. Hyperion, a space-based imaging spectrometer. IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1160-1173
  68. 68.
    Qi W C, Zhang X, Wang N, Zhang M and Cen Y. 2019. A spectral-spatial cascaded 3d convolutional neural network with a convolutional long short-term memory network for hyperspectral image classification. Remote Sensing, 11(20): 2363
  69. 69.
    Sakamoto Y, Sugimura N, Fukuda K, Kuwahara T, Yoshida K, Kurihara J, Fukuhara T and Takahashi Y. 2016. Development and flight results of microsatellite bus system for RISING-2. Transactions of the Japan Society for Aeronautical and Space Sciences, 14(30): Pf_89-Pf_96 [DOI 10.2322/tastj.14.pf_89]
  70. 70.
    Sang B, Schubert J, Kaiser S, Mogulsky V, Neumann C, Förster K P, Hofer S, Stuffler T, Kaufmann H, Müller A, Eversberg T and Chlebek C. 2008. The EnMAP hyperspectral imaging spectrometer: instrument concept, calibration, and technologies//Proceedings Volume 7086, Imaging Spectrometry XIII. San Diego, California, United States: SPIE: 708605 [DOI: 10.1117.12.794870]
  71. 71.
    Schaepman M E, Jehle M, Hueni A, D'Odorico P, Damm A, Weyermann J, Schneider F D, Laurent V, Popp C, Seidel F C, Lenhard K, Gege P, Küchler C, Brazile J, Kohler P, De Vos L, Meuleman K, Meynart R, Schläpfer D, Kneubühler M and Itten K I. 2015. Advanced radiometry measurements and Earth science applications with the Airborne Prism Experiment (APEX). Remote Sensing of Environment, 158: 207-219
  72. 72.
    Schmit T J, Griffith P, Gunshor M M, Daniels J M, Goodman S J and Lebair W J. 2017. A closer look at the ABI on the GOES-R series. Bulletin of the American Meterorological Society, 98(4): 681-698
  73. 73.
    Seidel F C, Stavros E N, Cable M L, Green R and Freeman A. 2018. Imaging spectrometer emulates Landsat: a case study with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Operational Land Imager (OLI) data. Remote Sensing of Environment, 215: 157-169
  74. 74.
    Shan Y H, Peng L and Sun Z H. 2006. Research of U. S. intellience, surveillance and reconnaissance systems. Journal of the National Defence University, (5): 91-92. (单月晖, 彭莉, 孙振华. 2006. 美军情报监视侦察系统发展研究. 国防大学学报(外军研究), (5): 91-92)
  75. 75.
    Shi Z D, Fang L and Zhou C X. 2014. Dispersive element based on grating and tunable Fabry-Perot filter in miniature spectrometer. Applied Optics, 53(1): 76-81
  76. 76.
    Shingu H, Homma K, Kurosaki H, Suzuki T and Yamamoto H. 2003. Field observation of surface conditions using LCTF spectropolarimeter//Proceedings Volume 5017, Sensors and Camera Systems for Scientific, Industrial, and Digital Photography Applications IV. Santa Clara, CA, United States: SPIE
  77. 77.
    Stuhlmann R, Rodriguez A, Tjemkes S, Aminou D M, Stark H and Schumann W. 2009. MTG-IRS: status, specifications and technical concept//Fourier Transform Spectroscopy 2009. Vancouver Canada: OSA
  78. 78.
    Sun T and Kelly K 2009. Compressive sensing hyperspectral imager//Computational Optical Sensing and Imaging. San Jose, California United States: OSA
  79. 79.
    Svedhem H, Titov D V, McCoy D, Lebreton J P, Barabash S, Bertaux J L, Drossart P, Formisano V, Häusler B, Korablev O, Markiewicz W J, Nevejans D, Pätzold M, Piccioni G, Zhang T L, Taylor F W, Lellouch E, Koschny D, Witasse O, Eggel H, warhaut M, Accomazzo A, Rodriguez-Canabal J, Fabrega J, Schirmann T, Clochet A and Coradini M. 2007. Venus express—the first European mission to Venus. Planetary and Space Science, 55(12): 1636-1652
  80. 80.
    Tanii J, Iwasaki A, Kawashima T and Inada H. 2012. Results of evaluation model of Hyperspectral Imager Suite (HISUI)//Proceedings of 2012 IEEE International Geoscience and Remote Sensing Symposium. Munich: IEEE: 131-134
  81. 81.
    Tanii J, Kashimura O, Ito Y and Iwasaki A. 2016. Flight model performances of HISUI hyperspectral sensor onboard ISS (International Space Station)//Proceedings Volume 10000, Sensors, Systems, and Next-Generation Satellites XX. Edinburgh, United Kingdom: SPIE: 100000A
  82. 82.
    Tao C N, Zhu H Z, Sun P, Wu R M and Zheng Z R. 2020. Hyperspectral image recovery based on fusion of coded aperture snapshot spectral imaging and RGB images by guided filtering. Optics Communications, 458: 124804
  83. 83.
    Tong Q X, Xue Y Q and Zhang L F. 2014. Progress in hyperspectral remote sensing science and technology in China over the past three decades. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1): 70-90
  84. 84.
    Tong Q X, Zhang B and Zhang L F. 2016. Current progress of hyperspectral remote sensing in China. Journal of Remote Sensing, 20(5): 689-707
  85. 85.
    Transon J, D’Andrimont R, Maugnard A and Defourny P. 2018. Survey of hyperspectral earth observation applications from space in the sentinel-2 context. Remote Sensing, 10(2): 157
  86. 86.
    Tripathi M K and Govil H. 2019. Evaluation of AVIRIS-NG hyperspectral images for mineral identification and mapping. Heliyon, 5(11): e02391
  87. 87.
    van Wagtendonk J W, Root R R and Key C H. 2004. Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity. Remote Sensing of Environment, 92(3): 397-408
  88. 88.
    Vane G. 1987. First results from the airborne visible/infrared imaging spectrometer (AVIRIS)//Proceedings Volume 0834, Imaging Spectroscopy II. San Diego, CA, United States: SPIE: 127-143 .
  89. 89.
    Vane G, Goetz A F H and Wellman J B. 1984. Airborne imaging spectrometer: a new tool for remote sensing. IEEE Transactions on Geoscience and Remote Sensing, GE-22(6): 546-549
  90. 90.
    Veraverbeke S, Stavros E N and Hook S J. 2014. Assessing fire severity using imaging spectroscopy data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and comparison with multispectral capabilities. Remote Sensing of Environment, 154: 153-163
  91. 91.
    Wagadarikar A, John R, Willett R and Brady D. 2008. Single disperser design for coded aperture snapshot spectral imaging. Applied Optics, 47(10): B44-B51
  92. 92.
    Wan L M, Lin Y Y, Zhang H S, Wang F, Liu M F and Lin H. 2020. GF-5 hyperspectral data for species mapping of mangrove in Mai Po, Hong Kong. Remote Sensing, 12(4): 656
  93. 93.
    Wang J, Zhou W and Yao L B. 2012. Review of current techniques and trends on foreign imaging reconnaissance. Journal of Naval Aeronautical and Astronautical University, 27(2): 199-204
  94. 94.
    Wang J Y and Xue Y Q. 1992. 64-Band airborne imaging spectrometer. Journal of Infrared and Millimeter Waves, 11(3): 181-188
  95. 95.
    Wang J Y and Xue Y Q. 1998. Airborne imaging spectrometers developed in China//Proceedings Volume 3502, Hyperspectral Remote Sensing and Application. Beijing, China: SPIE
  96. 96.
    Wang Y. 2015. Study of spectral imaging system based on filter. Changchun:
  97. 97.
    Wang Y M, Jia J X, He Z P and Wang J Y. 2016. Key technologies of advanced hyperspectral imaging system. Journal of Remote Sensing, 20(5): 850-857
  98. 98.
    Wei L Q, Xiao X Z, Wang Y M, Zhuang X Q and Wang J Y. 2017. Research on the shortwave infrared hyperspectral imaging technology based on Integrated Stepwise filter. Infrared Physics and Technology, 86: 90-97
  99. 99.
    Williams F L. 2003. The spectral characterization of the Cross-track Infrared Sounder (CrIS) engineering model: updated methodology and initial test results//Proceedings Volume 5152, Infrared Spaceborne Remote Sensing XI. San Diego, California, United States: SPIE
  100. 100.
    Xiao X Z. 2015. Research of Stepwise Integrated Filter Based Infrared Spectral Imaging. Shanghai:
  101. 101.
    Xu Y, Xie S B, Liang J, Sun D X, Qi N M and Liu Y N. 2020. A novel method to remove interference fringes for hyperspectral SWIR imagers. IEEE Transactions on Geoscience and Remote Sensing, 58(11): 7580-7588.
  102. 102.
    Yan C X. 2013. TG-1 hyperspectral imager. China Science and Technology Achievements, (11): 82-83 (颜昌翔. 2013. 天宫一号高光谱成像仪. 中国科技成果, (11): 82-83) [DOI: 10.3772/j.issn.1009-5659.2013.11.033]
  103. 103.
    Yarbrough S, Caudill T R, Kouba E T, Osweiler V, Arnold J, Quarles R, Russell J, Otten III L J, Jones B A, Edwards A, Lane J, Meigs A D, Lockwood R B and Armstrong P. 2002. MightySat II.1 hyperspectral imager: summary of on-orbit performance//Proceedings Volume 4480, Imaging Spectrometry VII. San Diego, CA, United States: SPIE: 186-197
  104. 104.
    Yuan J W, Wu C, Du B, Zhang L P and Wang S G. 2020. Analysis of landscape pattern on urban land use based on GF-5 hyperspectral data. Journal of Remote Sensing, 24(4): 465-478
  105. 105.
    Yuan X K. 2004. Present and future status of space microwave imaging radar. Space Electronic Technology, (1): 1-6, 14
  106. 106.
    Zhang Y. 2016. Optical Technology of Long-wave Infrared Hyperspectral Imaging. Shanghai:
  107. 107.
    Zhao X, Xiao Z Q, Kang Q, Li Q and Fang L. 2010. Overview of the Fourier transform hyperspectral imager (HSI) boarded on HJ-1A satellite//Proceedings of 2010 IEEE International Geoscience and Remote Sensing Symposium. Honolulu, HI: IEEE: 4272-4274
  108. 108.
    Zheng Q B and Wei J. 2004. Medium Resolution Imaging Spectro-radiometer (MRIS) on SZ-3 Spacecraft. Hangzhou: Chinese Society of Optics, Zhejiang Optical Society, Zhejiang University. (郑亲波, 危峻. 2004. SZ-3中分辨率成像光谱仪//大珩先生九十华诞文集暨中国光学学会2004年学术大会论文集. 杭州: 中国光学学会, 浙江省光学会, 浙江大学)
  109. 109.
    Zhu W, Wang X, Huang Y, Du P J and Tan K. 2020. Reweighted sparsity regularized deep nonnegative matrix factorization with total variation toward hyperspectral unmixing. Journal of Remote Sensing, 24(4): 401-416

Lire l'article complet

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website